Journal of Healthcare Engineering · Vol. 6 · No. 4 · 2015 Page 739–777 739

Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic

ReviewOlufemi Adeluyi and Jeong-A Lee

Department of Computer Engineering, Chosun University, Seosok Dong, Gwangju, Korea

Submitted March 2015. Accepted for publication July 2015.

ABSTRACTThe rising cost of healthcare and the increased senior population are some reasons for thegrowing adoption of the Personalized Health Monitoring (PHM) systems. Medical VirtualInstruments (MVIs) provide portable, flexible, and low-cost options for these systems. Oursystematic literature search covered the Cochrane Library, Web of Science, and MEDLINEdatabases, resulting in 915 articles, and 25 of which were selected for inclusion after a detailedscreening process that involved five stages. The review sought to understand the key aspectsregarding the use of MVIs for PHM, and we identified the main disease domains, sensors,platforms, algorithms, and communication protocols for such systems. We also identified the keychallenges affecting the level of integration of MVIs into the global healthcare framework. Thereview shows that MVIs provide a good opportunity for the development of low cost personalizedhealth systems that meet the unique instrumentation requirements for a given medical domain.

Keywords: personalized health monitoring, medical instrumentation, telemetry,electrocardiogram (ECG), electroencephalography (EEG)

1. INTRODUCTIONPersonalized Health Monitoring (PHM) refers to long term monitoring that is performedby a novice patient in an uncontrolled environment, such as his/her home [1]. It is averitable tool that supports not only the monitoring of a patient’s health status, but alsothe transition from a hospital-based, physician-centered healthcare delivery system, toone that is home-based and patient-centered. This transition has become necessary in thewake of challenges such as rising healthcare costs, dwindling healthcare budgets, agrowing proportion of senior citizens in developed societies, and a growing need formedical systems personalized to suit the user specific needs [2, 3, 4].

The feasibility and effectiveness of PHM depends on the availability of a pragmaticapproach for providing medical instruments at the patient’s home, similar to the traditionalinstruments found at a hospital. Unfortunately, many of these instruments are expensive

*Corresponding author: Jeong-A Lee, Department of Computer Engineering, Chosun University, 375 Seosuk-Dong, Dong-Gu, Gwangju, 501-759, Korea. Phone: +82622307711. Fax: +82622307755. E-mail:jalee@chosun.ac.kr. Other author:adeluyi@chosun.kr.

740 Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

and inconveniently bulky, and providing them at the patient’s home would nullify a numberof potential benefits of PHM, including its low cost and portability. Virtual instruments canprovide these tools to patients without forgoing the benefits of PHM.

A virtual instrument is an instrument that utilizes a hardware-software approach tosystem implementation. It takes advantage of the high performance of hardware andhigh flexibility of software to greatly reduce the size of the corresponding traditionalinstrument without sacrificing much of its functionality [5,6]. The hardware portioncomprises the sensor, display, and memory, while the software part is made up of theprocessing and interface modules. These instruments can be deployed in severalcontexts and their specific use essentially depends on the creativity of the designer [7].Medical Virtual Instruments (MVIs) are virtual instruments that are used within thecontext of medicine. They approach home-based health monitoring in a way thatemphasizes system re-use, modularity, adaptability, and user-defined instrumentation.For this review, we are interested in MVIs that are used for PHM. These systems areexpected to monitor patients’ health and support medical tests in key domains ofmedicine [8–10].

The objective of this systematic review is to give an overview of the current body ofwork covering the use of virtual instruments for personalized health monitoring. Theemphasis is on the identification of key architectures used for MVIs in terms of the typeof sensors, architecture, modality, communication interface, and network model.Second, it describes the important application domains of MVI, its level of adaptation,and the common algorithms utilized. Third, it outlines the key outcomes of using MVIsfor PHM as well as the current challenges and the anticipated future research directionfor the field.

The remainder of this review is organized as follows:In Section 2, we present theMaterials and Methods used to carry out the review. In Section 3, we outline the Resultsof the most common features of PHM-based medical instruments. We then discuss theresults in Section 4, and conclude in Section 5.

2. MATERIALS AND METHODS2.1. Search StrategyA systematic literature search was performed to identify studies on MVI using thefollowing databases: MEDLINE (1996–August 2014), Web of Science (1973–August2014) and Cochrane Library (1992–August 2014). The relevant fields and researchareas were identified and the following search query was used without limitations onthe year or type of publication:

For MEDLINE:

Query: (“user-computer interface”[MeSH Terms] OR (“user-computer”[All Fields]AND “interface”[All Fields]) OR “user-computer interface”[All Fields] OR(“virtual”[All Fields] AND “systems”[All Fields]) OR “virtual systems”[All Fields])AND (“instrumentation”[Subheading] OR “instrumentation”[All Fields]) OR(“equipment and supplies”[MeSH Terms] OR (“equipment”[All Fields] AND“supplies”[All Fields]) OR “equipment and supplies”[All Fields] OR “device”[AllFields]) AND (“telemedicine”[MeSH Terms] OR “telemedicine”[All Fields]) ANDMonitoring[All Fields].

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For Web of Science and Cochrane Library:Query: (“Virtual Systems” AND “Instrumentation” OR “Device” AND

“Telemedicine” AND “Monitoring”).The search resulted in 915 articles, with 642 from MEDLINE, 233 from Web of

Science, and 40 from the Cochrane Library.

2.1.1. Selection ProcessOur selection process was based on the following 5-stage strategy:

i. Deletion of doublesii. Title scaniii. Abstract scaniv. Cursory full text scanv. Detailed full text scan

We started by deleting the 46 articles that were duplicated before a screening basedon their titles. Titles that indicated contents different from research related to PHM andMVI were discarded. There were 573 articles discarded at this stage, leaving a total of296 articles for the abstract-scan stage. During the abstract scan, we eliminated articlesthat did not align with the theme of our review, and 127 articles were filtered out atthis stage.

The remaining 169 articles were subjected to a cursory scan of the full text, whichessentially involved identifying the sections, as well as reading the introduction,discussion, and conclusion sections. Articles that did not give sufficient details onthe items listed in our objectives were discarded. After this stage, there were 56articles that were subjected to a detailed full text scan, and 39 of which werediscarded. The criteria included those listed in the previous stage. A preference wasalso given to articles that covered multiple domains, those that addressed uniqueapplications, and those that described the instrumentation process in some detail.After all these stages, we had 25 articles that were included in this review. Adescription of each of these articles is given in Appendix A. The search strategy isshown in Figure 1.

3. RESULTSBased on the analysis of the 25 research articles included in this review, we identifieda number of key features that characterize the use of MVIs for PHM. These features willbe discussed in the subsequent subsections as follows:

• Architecture• Application• Outcomes

3.1. Architecture of MVIsThe architecture describes the hardware portion of the MVI and the communicationinterface. It comprises the sensors, system platform, and the communication interfaceutilized for both the local and remote ends of the MVI.

3.1.1. Sensors and SensingThe sensors capture the analog bio-signals from the patient and condition them forfurther processing. Table 1 shows the parameters and types of sensors used in theselected articles and the percentage of the 25 studies where they were used.

At 40.0%, Electrocardiogram (ECG) sensors represent the most extensively usedsensors for MVIs. As shown, when the Cardiac Implantable Electronic Device (CIED)sensors are included, the percentage rises to 48.0%, implying that close to half of MVIsystems monitor heart signals. Blood pressure (BP) sensors and accelerometers are next inprevalence at 24.0% each. It is interesting to note that a number of these sensors measuresignals as a proxy for another signal of interest and are thus known as virtual sensors.

Virtual sensors [17,18] are quickly becoming an important part of the MVIarchitecture. They refer to sensors that are based on software rather than hardware andthey infer their readings from the relevant hardware sensor(s). These virtual sensorsenable patients to monitor bio-signals for which it is either impractical to have accessto the signal of interest or for which the sensors or related equipment may be tooexpensive. From the reviewed articles, there were 12 cases, or 48.0%, that used virtualsensing. The actual sensors used for this process are listed in Table 2.

742 Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

Search query

Cochranelibrary

40 233

915

Stage 1(Duplicates)

Stage 2(Title scan)

Stage 3(Abstract)

Stage 4(Cursory full text)

Stage 5(Detailed full text)

869Include

296Include

169Include

56Include

25Include

642

46

573

127

113

31

Discard

Discard

Discard

Discard

Discard

Web ofscience

Medline

Figure 1. Search strategy.

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Table 1. Sensors and parameters measured in MVIs

S/No. Sensor Cases %

1 Electrocardiogram [11, 14, 20, 21, 22, 23, 24, 27, 30, 31] 10 40.02 Blood pressure [11, 14, 21, 25, 27, 29] 6 24.03 Accelerometer [14, 22, 26, 27, 29, 32] 6 24.04 Oxygen saturation [14, 21, 27, 29] 4 16.05 Temperature [14, 25, 29, 30] 4 16.06 Microphone [15, 28, 30, 37] 4 16.07 Posture [14, 21, 29] 3 12.08 Pressure [16, 25, 35] 3 12.09 Weight [11,21] 2 8.010 Cardiac implantable electronic device [12,19] 2 8.011 Gyroscope [14,32] 2 8.012 Blood glucose [33,36] 2 8.013 Photodiode [29] 1 4.014 Surface electromyography [13] 1 4.015 Electroencephalography [34] 1 4.016 Tilt [22] 1 4.017 Camera [30] 1 4.018 Pedometer [32] 1 3.819 Gastrocnemius expansion [32] 1 3.821 Electro dermal activity [37] 1 3.822 Chest impedance [21] 1 3.8

Table 2. Examples of inferred parameters in MVIs

S/No. Inferred Parameters Actual Sensor No of Cases

1 Respiratory rate [14, 26 29, 35] ECG, accelerometer, pressure 42 Heart rate [29,31] ECG 23 Respiratory input impedance [16] Pressure 14 Drowsiness [37] Electro dermal activity (EDA) 15 Gait analysis [32] Gastrocnemius expansion

Measurement unit (GEMU) 16 Parkinson’s disease

Progression [15] Microphone 17 Obstructive sleep apnea syndrome

(OSAS) [28] Microphone 18 Consciousness awareness [34] EEG 1

Total 12

3.1.2. PlatformVirtual instrumentation was introduced to the consumer market in the late 1980s by acompany known as National Instruments through a product called LabVIEW(Laboratory Virtual Instrument Engineering Workbench). Other products currentlyavailable in the virtual instrumentation space include the Simulink software fromMathworks and the BioMobius [38] open source biomedical platform developed by theTechnology Center for Independent Living (TRIL), Ireland. The Reconfigurable VirtualInstrumentation (RVI) open source platform of the International Center for TheoreticalPhysics (ICTP) in Trieste, Italy is another similar platform. It uses a FieldProgrammable Gate Array (FPGA) as its reconfiguration engine [39] and has been usedto develop a neural monitoring system. Essentially, most of these virtual instruments arerequired to run on a dedicated general-purpose computer.

MVIs are developed to support PHM systems for which portability is an importantrequirement. Unlike the general virtual instrument approach, most MVIs do not use aPC as their platform, but instead a custom device [14–16, 20–23, 25–29, 32, 33–37] ora mobile phone/personal digital assistant (PDA), [11, 13, 24, 30, 31, 33] as shown inTable 3. The two cases of Cardiac Implantable Electronic Devices (CIEDs) [12,19]were not included since they only use mobile phones for communicating with a remotesystem.

3.1.3. Network Models and Communication SystemAll of the MVIs in the reviewed articles were based on a client-server network model.In most systems, the sensed signals were forwarded from the local (patient) end to anaccess point device in close proximity to the sensors for onward transmission to aremote server at the remote (physician) end. In many cases, the platforms described inthe previous section are used as the access points.

One trend worth noting about MVI network models involves the direct connectionbetween the output of the bio-signals and a remote webserver or cloud service, ratherthan a connection to a specific remote server on the physician’s end. Five of thereviewed articles [22, 28, 31, 36, 37] used such a model. A number of advantages canbe derived from this approach. One such advantage is the potential of “geographicallydecoupling” the bio-signals [36]. In other words, it reduces the mobility restrictions on

744 Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

Table 3. Platforms and protocols used in MVIs

PlatformsCustom Devices PDA/Phone Laptop/PC

Cases 17 6 1Local-MVI Communication Protocols

Wireless RS-232 OthersCases 17 2 3Remote MVI Communication Protocols

Cellular WiFi DSLCases 14 9 2

the patients since their signals can be streamed to the webserver while they move aboutfreely. Another advantage is that many different authorized personnel, such as thephysician and the caregiver, can simultaneously view the patient’s signals. Theapproach can also exploit the memory and processing capabilities of a web or cloudservice while reducing the computational complexity of the MVI at the patient’s end.

Communication via MVIs can either occur within the local patient modules orbetween the physician’s remote MVI and the patient’s local MVI. The communicationin the former is known as local-MVI communication, while the latter is known asremote-MVI communication. The remote MVI is similar to the local MVI, withexception to the inclusion of the remote’s bio-signal sensors. Much of its functionalityinvolves the analysis of the signals, which results in a greater storage and processingcapacity in the remote MVI.

The wireless protocol was the most common for local-MVI communication and wasused in 73.9% of the cases (Table 3). This included 26.1% for Bluetooth [11, 21, 30, 31,33, 34], 8.7% for ZigBee [22,24], and 39.1% for unspecified wireless protocols [12, 19,20, 23, 25, 28, 29, 32, 33]. RS-232 protocols were used in 8.7% of the cases [14,37] andanother 17.4% used other methods [15, 16, 20, 36]. Some articles did not specify thelocal-MVI communication protocol [26, 27, 35].

The cellular networks were the most common approach for intra-MVIcommunication, used in 56% of the cases [11–14, 19–21, 24, 27–30, 33, 36]. It wasfollowed by the wireless approach at 36%, 28% of which were with Wi-Fi [13, 16, 20,22, 24, 31, 32] and the remaining 8% with unspecified wireless techniques [16,23]. DSL techniques were used in 8% of the references [16,21]. The details areshown in Table 3. Some articles used multiple techniques for remote-MVIcommunication while others failed to specify the local-MVI communication protocol[17, 25, 26, 27, 34, 35, 37].

3.2. MVI ApplicationsMVI applications refer to the disease domains, adopted modality, level of systemadaptability, and algorithms that govern the operation of the instrument.

3.2.1. Disease DomainsMVIs can be used for, but are not limited to medical research applications, clinicalapplications, medical design development, healthcare information managementsystems, and mathematical modeling of physiologic systems [7]. However, from theanalysis of the research articles in this review, we found that the use of MVIs for PHMfocuses on certain specific disease domains. These domains are shown in Figure 2. TheCardiovascular Disease (CVD) domain accounts for over half of these cases. This isunderstandable since CVDs are the largest single contributor to global mortality [8].The constituent monitoring scenarios classified under the cardiovascular domain areshown in Table 4.

Fitness monitoring refers to cases where the MVI did not target a specific domain.These cases were basically for monitoring general health and fitness. They accountedfor about a quarter of the cases. The musculoskeletal domain addressed areas like fall

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detection, gait analysis, and back pain. The cases classified under the neurologicdomain focused on mental health monitoring using EEG sensors in the first case [24],and in the second case, an EDA sensor [27] for monitoring drowsiness. In a caseinvolving the hormonal disease domain, blood glucose levels were monitored for themanagement of diabetes [23].

3.2.2. ModalityThe modality refers to the expected effect on the state of health of the patient. 92.3% ofthe cases reviewed focused solely on extracting, analyzing, and reporting a patient’sbio-signals. Only 2 MVI systems (7.7%) triggered some form of therapeutic activity inresponse to the results of the analysis. The first case controlled the delivery of insulinto the diabetic patient [23] and the second case involved a stimulation to help keep thesleeping patient from snoring [28].

3.2.3. System AdaptabilityImplicit in the name ‘personalized health monitoring’ is a need to have systems that areindividually suited to the needs of a patient. This requires that the systems support somelevel of adaptability. The high level of flexibility in MVIs makes them a ready fit for such

746 Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

Cardiovascular52%

Fitnessmonitoring

24%

Musculoskeletal14%

Neurologic7%

Hormonal3%

Figure 2. MVI disease domains.

Table 4. Monitoring scenarios for the cardiovascular domain

S/No. Monitoring Scenario No of Cases

1. Heart monitoring [12, 19, 20, 23, 24, 31, 34, 35] 82. Chronic heart failure [11,21] 23. Spirometry [29] 14. Hypertension [25] 25. Blood flow velocity [18] 16. Obstructive sleep apnea [28] 1

Total 15

a requirement. A total of 11 (42.3%) of the reviewed articles supported some level ofadaptation [12, 13, 21, 23, 27, 28, 29, 31, 33, 34, 36], most of which were adaptation atthe communication and architectural levels rather than at the patient level. Five of the sixcases with adaptation at the subject level [12, 28, 29, 33, 34] were based on the subject’sclinical profile. The sixth case was based on the activity state of the subject [27].

3.2.4. AlgorithmsAlgorithms help patients and physicians make sense of the bio-signals generated by thesubject. The type of disease domain and the amount of resources available on the MVIare some of the determinants of the type of algorithm used. Table 5 shows a list of thealgorithms that were explicitly stated by the authors of the reviewed articles. The Pan-Tompkins algorithm was featured in the most number of cases (3) and was used forMVIs targeting the cardiovascular domain, due to their real-time suitability andresource-light processing of ECG signals. Peak detection algorithms were also used in3 of the MVIs.

3.2.5. Outcomes/ResultsSome key outcomes were reported in the studies on the use of MVIs for PHM whencompared to traditional medical instruments. These outcomes were based on the threeareas listed below:

1. Effect on utility of the medical device. Common reported outcomes included:a. Miniaturization of traditional medical instruments [13, 14, 16, 27, 28, 29, 32]b. Reduced operator bias; enabling a more quantitative based analysis [13, 15,

33, 34]c. Monitoring of device parameters [19]

2. Effect on healthcare. Common reported outcomes included:a. Early detection of diseases [11, 12, 21]b. Lower healthcare cost [23, 35, 36]c. Reduced need for follow up and hospitalization [11,12]

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Table 5. Common algorithms used in MVIs

Algorithm Cases

Pan-Tompkins [21, 24, 27] 3Peak detection [14, 26, 34] 3Thresholding [14,15] 2Wavelet transform [31,35] 2Correlation [18] 1Fuzzy-based [20] 1Machine learning [30] 1Least squares [16] 1Edge detection [26] 1

3. Effect on the perception and satisfaction of the patient:a. Patient feedback on usefulness of system [21,27]

Many of the studies reported that the MVI approach led to a positive effect on theutility of the medical devices [11, 12, 13, 14, 15, 16, 19, 21, 23, 27, 28, 29, 32, 33, 34,35, 36]. Miniaturization was mentioned in these studies as the most importantmotivation for using MVIs. MVIs enable physicians to quantify the progression ofdisease and enable them to make informed choices that are not affected by the bias ofoperators that utilized equivalent traditional medical instruments. CIEDs and otherimplanted devices can be monitored with MVIs, as confirmed by one of the studiesregarding the monitoring of leads, battery level, and device impedance [19].

The effect of MVIs on healthcare was another key category mentioned. Earlydetection of disease was the most important goal of the PHM systems mentioned in thiscategory. Some studies also showed that the use of MVIs can lead to a reduction in thecost of the medical equipment and, by extension, also lead to a reduction in healthcarecosts. Systems ranging anywhere between $1.000 and $50,000 could be replaced witha system costing approximately $25. A $1,000, 11 sensor system could be replaced witha much cheaper, one sensor system [36]. Two studies also mentioned a reduction in theneed for hospitalization and follow up appointments as an outcome for this category.

The third category of outcomes related to the perception and satisfaction of thepatient. A few studies handed out questionnaires to the users of the system to assess the level of its perceived usefulness. In all of the reported cases, the patients found thesystems useful.

4. DISCUSSIONThis review discusses systems that have involved the use of medical virtual instrumentsfor PHM. PHM systems can support the paradigm shift of global healthcare and itsevolving focus from curative to preventive medicine. However, in order to ensure thatPHM systems can support non-fitness related health monitoring, they must enablemonitoring that addresses a wider range of health challenges. To achieve this, it isimportant to ensure that these systems incorporate sophisticated medical instruments.Unfortunately, such instruments tend to be complicated, large, and expensive, creatingchallenges for the PHM systems to provide simplicity, portability, and costeffectiveness. MVIs provide a veritable tool for bridging the gap between the needs ofPHM systems and hospital-based health-monitoring systems that utilize traditionalmedical instruments.

Although the articles reviewed highlight several advantages of the MVI approach,much research is still needed in order to make MVI-based PHM systems attain a levelof dependability and utility comparable to those offered by traditional medicalinstruments in the clinical setting. For one, on the issue of personalization, less than aquarter of the articles supported any kind of system adaptation that was based on theprofile of the patient. Furthermore, the desired level of adaptability will be attainedwhen MVIs can reconfigure themselves according to the genomic characteristics of thesubject. In the same vein, MVIs need to advance beyond the current state of being used

748 Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

merely for patient monitoring. They need to start supporting therapeutic applicationsfor PHM systems.

System reliability and quality of service need to be guaranteed for patients andphysicians so that each can have the same level of confidence in MVIs as they do intraditional medical instruments. Issues like ensuring accurate medical alerts and, in effect,reducing false alarms are the initial areas that need to be addressed. MVIs are not asstandardized as traditional medical instruments and may still require extended, and oftencomplicated training sessions in order to provide reliable results. This can reduce theusability and adoption level of the systems. The approach in one of the studies involvedthe employment of an MVI that reduced the need for system training to the barestminimum [34]. Such an approach can aid in the evolution and acceptance of MVIs.

The high levels of sensitivity of health records make privacy, security, andauthentication issues of prime importance. Six of the reviewed articles (23.1% of thecases) used encryption techniques to protect patient data. Another article [33]investigated an interesting concept of e-consents to support privacy. Virtual sensing canprovide an interesting approach to privacy and confidentiality. For instance, a cloudservice library containing several MVIs can be developed with the ability to screen fordifferent types of diseases. An interested subject can then use a virtual sensing approachto generate a proxy signal without having to visit the hospital to get the standard bio-signals required for the given test. The subject can then upload this signal to the diseasesearch engine and retrieve the results that indicate the presence or absence of thediseases that were screened for. In order to increase confidentiality, the system can bedeveloped to use the biometrics of the subject for the bio-signal upload and the resultdownload processes.

The requirement for mobility in PHM systems comes with a need for flexible andless power- hungry applications. Generally, these systems use batteries as their powersources. However, continuous real-time monitoring easily drains batteries. Alternativepower options can enhance the utility value of MVIs. For example, power-harvestingtechniques prove to be useful. Other options include power via wireless magnetictelemetry. [25].

More research is needed to expand the variety of MVIs beyond the systems thatmainly focus on cardiovascular health. For example, mental health is becoming a globalhealthcare concern. It has been reported that one in four people now experience amental health issue in their lifetime [9]. Surprisingly, only one of the reviewed studiesthat used MVIs were based on bio-signals from the brain [34]. Researchers should betaking advantage of the low-cost option provided by EEG sensors to monitor brainsignals. There are encouraging signs that MVIs are growing in scope and sophistication[40]. Examples include their use for biomedical imaging [41], monitoring Parkinson’sdisease [42,43], and respiratory disease [44,45].

Non-invasiveness and non-intrusiveness are two words that describe the desired typeof sensors for MVIs. The sensors play an important role in determining the quality ofthe signals being monitored. However, bio-signals from a number of the non-invasiveor non-intrusive sensors do not generally provide health information as detailed as thosefrom invasive tests performed at a hospital. For example, 644 tests are performed with

Journal of Healthcare Engineering · Vol. 6 · No. 4 · 2015 749

body fluids [10]. One would not expect MVIs to carry out as many tests, but it stillshows that greatly increasing the number of possible tests would aid in the developmentof MVIs. Virtual sensing provides an opportunity to use the current non-invasive bio-signals to simulate the more invasive bio-signals. For example, one study obviates theneed for expensive and complex polysomnography equipment by inferring its readingsfrom a virtual sensor [35]. The virtual sensor was based on a pressure sensor embeddedin a pillow. Also, capacitive sensing techniques mentioned in [31] can be useful in thedevelopment of more non-intrusive sensors.

A typical healthcare workflow includes, but is not limited to aspects like diagnosis,decision-making, treatment, and administrative procedures [46]. The use of MVIswould affect at least two of these aspects–most likely diagnosis and decision-making.As such, it may be necessary to redesign the healthcare workflow and organizationalmodels to accommodate the use of MVIs. In a similar vein, reimbursement schemesneed to include workflows that are based on the use of MVIs.

The results of this work are in line with some of the orientations described in EuropeanCommunity’s report entitled “COMMUNICATION FROM THE COMMISSION TOTHE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMICAND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS–ontelemedicine for the benefit of patients, healthcare systems and society” [47]. Forinstance, the report mentioned the importance of integrating telemedicine into thehealthcare workflow. It also identifies the support of seniors, the reduction of hospitalvisits, and the reduction in healthcare costs as potential benefits of telemedicine and healthmonitoring.

One more thing to note about the challenges and future perspectives of MVI in PHMrelates to the potential of webservers as a tool for decoupling the monitoring processfrom the limitations imposed by location. By taking advantage of miniaturizedintegrated circuits and microprocessors, MVIs can provide sensors that immediatelytransfer the signals to an always-on, local or remote webserver. This would enable real-time monitoring and allow all authorized persons to simultaneously view the signals. Itwould also allow MVIs to take advantage of the huge memory and processingcapabilities of cloud computing.

There are a number of limitations in this review. First, the studies were based onprojects managed by universities and other research institutes, not by hospitals. As such,not many of the covered systems have become mainstream commercial solutionscontinuously deployed in real-life environments. Second, there were few clinical trialsand many were simulation studies. For those assessed with human subjects, many hadsmall sample sizes and were based on a short test period. However, a number of trialsinvolving CIEDs had large sample sizes. For example, 1650 patients in 75 Italiancenters were monitored for periods ranging from 10-31 months [48].

Thirdly, only three of the studies gave details of the cost of implementation, makingit difficult to directly compare with equivalent traditional medical instruments.Furthermore, with the rapid evolution of medical research and technology, there couldbe many implemented projects whose findings have not yet been published. As such, itis likely that this review has underestimated some of the current applications andtechniques involving MVIs.

750 Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

5. CONCLUSIONSIn this systematic review, we have identified the main modules of PHM-orientedinstrumentation and the issues that determine their utility. To a large extent, currentPHM systems are mainly used for fitness monitoring and niche applications. Thisshould change, and PHM instrumentation should support a greater level of adaptabilityand dependability in order to become a system of choice in today’s healthcare toolchain. The review shows that research on the use of MVIs in PHM is still in its infancywith many challenges to overcome. However, despite the challenges, it holds the uniquepromise of providing the patients with customizable medical instrumentation, at anaffordable cost, in the comfort of their homes.

ACKNOWLEDGEMENTThis work was supported in part by research funds from Chosun University(2014–2015).

CONFLICT OF INTERESTThe authors indicate no potential conflicts of interest in this work.

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vice

can

be

used

Res

pira

tory

Tech

niqu

eco

nsec

utiv

efo

r un

supe

rvis

ed

inpu

t(F

OT

) de

vice

daily

hom

eas

sess

men

t of

impe

danc

e (Z

rs)

was

for

mea

sure

men

ts.

airw

ay

mea

sure

d af

ter

a 5H

zun

supe

rvis

edR

educ

es th

eob

stru

ctio

n ov

er

pres

sure

stim

ulus

mon

itori

ng to

dim

ensi

ons

ofpr

olon

ged

peri

ods.

from

a lo

udsp

eake

rre

plac

ecu

rren

t FO

TT

he m

axim

um

was

tran

smitt

edsu

perv

ised

devi

ce.

erro

r w

as 1

0%.

to th

e pa

tient

spir

omet

ry.

36 c

onse

cutiv

e

thro

ugh

a se

lf-m

ade

Use

ful f

orda

ily h

ome

mes

h-ty

pedi

agno

sis

and

mea

sure

men

ts.

Pneu

mot

acho

grap

hst

agin

g of

Ext

ensi

ve te

sts

(PN

T).

obst

ruct

ive

on 7

sub

ject

s.

Zrs

was

com

pute

ddi

seas

es (

like

Lim

itatio

n:

usin

g th

e L

east

CO

PD a

ndO

nly

1 C

OPD

squa

res

algo

rith

mas

thm

a).

patie

nt w

as

and

tran

smitt

edus

ed in

the

over

the

Inte

rnet

.te

sts.

Com

mun

icat

ion

was

bas

ed o

n

GPR

S, D

SL o

r W

iFi

(dep

endi

ng o

n

avai

labi

lity)

.

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

Enc

rypt

ion

was

bas

ed

on P

ublic

Key

Alg

orith

m.

Rea

ltim

e or

sch

edul

ed

tran

smis

sion

opt

ions

wer

e av

aila

ble.

Rel

iabl

e an

d

stan

dard

ized

TC

P/IP

con

nect

ion

was

est

ablis

hed

by u

sing

SSH

.

7.St

iche

rlin

g 20

09Sw

iss

Med

ical

CIE

D; b

ased

on

an o

ptiv

olR

emot

eIt

was

a s

urve

y.

et a

l. [1

9]W

eekl

y.se

nsor

that

mea

sure

dm

onito

ring

intr

atho

raci

c im

peda

nce

of I

CD

s fo

r ea

rly

upon

the

accu

mul

atio

n of

dete

ctio

n of

intr

apul

mon

ary

flui

d.di

seas

e an

d de

vice

GSM

was

use

d fo

ran

omal

y. I

t was

rem

ote

mon

itori

ng.

also

use

d to

num

ber

of p

atie

nt

visi

ts to

the

hosp

ital.

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

8.W

ang

2010

IEE

E W

irel

ess

It u

sed

a 3-

lead

EC

GSe

cure

and

T

he s

yste

mT

he te

sts

wer

e ba

sed

et a

l. [2

0]C

omm

unic

atio

ns.

sens

or w

ith a

tran

smis

sion

reso

urce

aw

are

allo

cate

d ex

tra

on s

imul

atio

n an

d re

al

rang

e of

100

m.

body

sen

sor

ener

gy r

esou

rces

time

test

s. T

his

It u

sed

a lo

w d

elay

netw

ork

to p

rote

ct th

elo

wer

ed e

nerg

y

adap

tive

encr

yptio

nar

chite

ctur

e fo

rim

port

ant p

ortio

nco

nsum

ptio

n an

d ga

ve

sche

me

depe

nden

t on

the

real

-tim

e he

alth

of th

e tr

ansm

itted

bette

r si

gnal

qua

lity

per

cond

ition

of

the

wir

eles

sm

onito

ring

bas

edsi

gnal

.en

ergy

use

d. A

utho

rs

chan

nel.

on u

nequ

alst

ated

that

sho

rter

QR

S

reso

urce

win

dow

s ca

n im

prov

e

allo

catio

ns.

real

-tim

e en

cryp

tion.

It u

sed

an o

n-bo

dy

heal

thno

de d

ata

term

inal

to p

roce

ss

and

tran

smit

sens

or d

ata.

9.Fa

nucc

i 20

13IE

EE

Tra

nsac

tions

on

7 Se

nsor

s: (

i) 3

-lea

d E

CG

Flex

ible

and

Con

figu

ratio

nO

ne o

r fe

w n

on-

EC

G s

imul

ator

s an

d

et a

l. [2

1]In

stru

men

tatio

n an

d(i

i) S

PO2

(iii)

BP

high

lypa

ram

eter

s:co

ntin

uous

dai

lyPh

ysio

net T

oolk

it w

ere

Mea

sure

men

t.(i

v) W

eigh

t (v)

Che

stco

nfig

urab

leA

larm

thre

shol

ds,

mea

sure

men

ts

used

for

ana

lysi

s.

impe

danc

e (v

i) R

espi

ratio

nsy

stem

for

tran

smis

sion

pol

icy,

wer

e m

ade.

Pre-

prot

otyp

e te

sts:

(vii)

Pos

ture

.m

onito

ring

sele

ctab

le

2 pa

tient

s, 1

mon

th te

st.

Loc

al-M

VI

Chr

onic

Hea

lthsy

mpt

oms.

Post

-pro

toty

pe te

sts:

com

mun

icat

ion:

Blu

etoo

thFa

ilure

(C

HF)

.It

als

o su

ppor

ted

30 p

atie

nts

with

Rem

ote-

MV

IIt

pro

vide

d al

erts

re

mot

eC

hron

ic H

eath

com

mun

icat

ion:

AD

SL o

rfo

r ab

norm

alco

nfig

urat

ion.

Failu

re d

isea

se.

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

mob

ile b

road

band

.he

art f

requ

ency

,R

esul

ts: <

3% a

ctiv

ity

HT

TPS

use

d fo

r se

curi

tyat

rial

fib

rilla

tion

mis

ses

(mos

tly in

the

and

encr

yptio

n.ep

isod

es, Q

RS

1st d

ays)

. <5%

fal

se

A p

roto

type

was

bui

lt an

dco

mpl

exes

posi

tive

alar

ms,

95%

the

Pan-

Tom

pkin

sex

ceed

ing

120

ms

patie

nts

foun

d th

e

algo

rith

m w

as u

sed

for

and

sign

s of

syst

em u

sefu

l and

99%

EC

G a

naly

sis.

myo

card

ial

patie

nts

wer

e sa

tisfi

ed

isch

emia

.w

ith s

yste

m.

10.

Lee

20

11Te

lem

edic

ine

The

sys

tem

use

d a

Mob

ile h

ealth

It u

sed

a 1-

hour

test

to

et a

l. [2

2]an

d e-

Hea

lth.

Nin

tend

o D

S G

ame

EC

G a

nd g

ait

ensu

re a

ppro

pria

te

Con

sole

as

the

plat

form

.m

onito

ring

wir

eles

s co

nnec

tivity

.

How

ever

, the

sys

tem

syst

em th

atT

he h

ealth

mon

itori

ng

can

also

use

a P

C o

r PD

A.

obvi

ates

dis

tanc

ete

st la

sted

for

ove

r

3-ch

anne

l EC

G, 3

-axi

sre

stri

ctio

ns.

24 h

ours

with

out

acce

lero

met

er (±3

g),

any

inte

rrup

tion.

Tilt

ing

(Sen

sors

).Pa

cket

loss

:

1 E

CG

pac

ket c

onsi

sts

of<

5% f

or d

ista

nces

less

64 E

CG

sig

nals

.th

an 2

0 m

, muc

h hi

gher

Zig

Bee

was

use

d fo

r th

ean

d in

crem

enta

l los

s

loca

l-M

VI

com

mun

icat

ion

beyo

nd 2

0 m

whi

le W

iFi w

as u

sed

for

For

pack

et e

rror

the

Rem

ote-

MV

Ira

te (

Pe):

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

com

mun

icat

ion.

The

W

hen

Pe =

0, n

o de

lay;

syst

em a

lso

incl

uded

Pe =

0.2,

25s

del

ay a

nd

a w

ebse

rver

.Pe

=0.

4, 1

57s

dela

y

(due

to th

e ne

ed f

or

retr

ansm

issi

on).

11.

Dilm

agha

ni

2011

IEE

E T

rans

actio

ns o

nE

CG

(Se

nsor

).Sy

stem

to m

onito

rW

PPU

can

be

The

stu

dy a

void

edSa

me

qual

ity o

f se

rvic

e

et a

l. [2

3]B

iom

edic

al C

ircu

itsIt

s W

irel

ess

Patie

ntpa

tient

s w

ithco

nfig

ured

for

ath

e us

e of

a P

C a

ndas

PC

bas

ed s

yste

ms

and

Syst

ems.

Port

able

Uni

t (Pl

atfo

rm)

chro

nic

dise

ases

vari

able

gai

nPD

A (

to r

educ

eat

a lo

wer

cos

t.

had

a w

ebse

rver

and

in th

eir

hom

es.

betw

een

500

cost

).

conn

ecte

d to

a c

entr

alan

d 10

00.

Syst

em o

bjec

tives

:

rem

ote

node

via

Int

erne

tE

limin

ate

the

need

acce

ss p

rovi

ded

by a

for

a PC

, elim

inat

e

Wir

eles

s A

cces

s Po

int

the

need

for

use

rs

Uni

t (W

APU

).to

con

figu

re th

e

syst

em, s

uppo

rt

auto

mat

ic

tran

smis

sion

of

sign

als,

low

er

cost

, and

incr

ease

ease

of

use.

12.

Hii

and

2011

Sens

ors

(Bas

el).

EC

G, S

mar

tpho

neM

obile

pho

neSu

cces

sful

eal

time

Chu

ng [

24]

cam

era

(Sen

sors

).ba

sed

real

tim

em

onito

ring

on

a

The

sys

tem

was

bas

ed o

nE

CG

mon

itori

ng.

test

bed

with

a

3 la

yers

, nam

ely:

It

took

adv

anta

gehu

man

sub

ject

.

(i)

Bod

y

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

Sens

or L

ayer

[E

CG

of th

e fa

lling

node

s on

bod

y]co

st a

nd r

isin

g

(ii)

Per

sona

l Net

wor

kco

mpl

exity

of

Lay

er [

Mob

ile p

hone

]m

obile

pho

nes.

(iii)

Glo

bal N

etw

ork

A Q

R c

ode

Lay

er.

scan

ner

was

use

d

Plat

form

: mob

ile p

hone

.to

det

erm

ine

Loc

al-M

VI

com

mun

ic-

patie

nt a

dher

ence

.

atio

n: Z

igB

ee.

Rem

ote-

MV

I

com

mun

icat

ion:

CD

MA

,

GSM

, 3G

or

WiF

i.

Mod

es: r

eal-

time

(im

med

iate

ly v

iew

able

on p

hone

) or

Sto

re-

and-

forw

ard

(20

byte

EC

G

data

and

sum

mar

y re

port

s

tran

sfer

red

to th

e re

mot

e

end)

. One

EC

G d

ata

pack

et

cont

aine

d 10

EC

G

sign

als

(pho

ne s

cree

n

coul

d di

spla

y 5

pack

ets,

equi

vale

nt to

5 s

of

data

).

Alg

orith

m: P

an-T

ompk

ins

(The

ana

lysi

s w

as f

or

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

QR

S pe

aks,

QT

and

RR

inte

rval

s).

The

cam

era

was

use

d fo

r

scan

ning

the

QR

bar

code

s

on m

edic

ine

pack

s.

13.

Cle

ven

2012

IEE

E T

rans

actio

ns

Cap

aciti

ve p

ress

ure

and

Wir

eles

s B

PA

bat

tery

less

T

he tr

ial u

sed

an

et a

l. [2

5]on

Bio

med

ical

te

mpe

ratu

re (

Sens

ors)

.m

easu

rem

ent.

syst

em th

at u

ses

anan

esth

esiz

ed s

heep

Eng

inee

ring

.M

easu

red

intr

aart

erte

rial

impl

ant i

nto

the

and

read

ings

wer

e

pres

sure

usi

ng a

n im

plan

tFe

mor

al a

rter

y. T

heco

mpa

red

to a

cons

istin

g of

a s

enso

rim

plan

t con

sist

s of

refe

renc

e ca

thet

er.

tip a

nd tr

ansp

onde

r a

pres

sure

sen

sor

The

sys

tem

had

an

com

mun

icat

ing

with

aan

d te

lem

etri

c un

it ac

cura

cy o

f ±1

.0

read

out s

tatio

n.an

d w

as p

lace

d m

mH

g an

d a

rang

e

Loc

al-M

VI

unde

r th

e sk

in.

of 3

0-30

0 m

mH

g.

com

mun

icat

ion:

Indu

ctiv

e co

uplin

g.

The

pre

ssur

e se

nsor

was

pow

ered

usi

ng

wir

eles

s m

agne

tic

tele

met

ry.

14.

Pitts

20

13Ph

ysio

logi

cal

Acc

eler

omet

er (

Sens

or).

Sim

ple

low

cos

tR

espi

rato

ry s

enso

rR

2 va

lues

mea

n

et a

l [26

]M

easu

rem

ents

.T

he a

lgor

ithm

was

bas

edde

vice

for

base

d on

clav

icul

ar r

espi

rato

ry

on p

eak

and

edge

mea

suri

ngcl

avic

ular

mot

ion.

rate

: 0.8

9 (l

ater

al)

dete

ctio

n.re

spir

ator

y ra

teA

cla

vicu

lar

and

0.98

(lon

gitu

dina

l),

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

The

sys

tem

pro

vide

dan

d in

ferr

ing

sens

or w

as u

sed

com

pare

d to

0.4

9

virt

ual s

ensi

ng o

fdi

seas

e if

the

rate

sinc

e it

gave

(tho

raci

c).

resp

irat

ory

rate

bas

edfa

lls o

utsi

de th

esi

gnal

s w

ith a

Syst

em w

as u

naff

ecte

d

on th

e lo

ngitu

dina

l12

-20

brea

dth/

grea

ter

ampl

itude

by b

ioel

ectr

ical

or

(Z)

axis

rea

ding

of

the

min

ute

rate

of

and

whi

ch w

ere

elec

trod

e pr

oble

ms.

acce

lero

met

er p

lace

d on

heal

thy

adul

ts.

mor

e co

nsis

tent

A 4

-min

bre

ath-

by-

the

patie

nt’s

cla

vicl

e.th

an th

orac

icbr

eadt

h te

st p

erio

d

sens

ors.

was

use

d.

The

sys

tem

was

test

ed o

n 8

volu

ntee

rs.

15.

Anl

iker

20

04IE

EE

Tra

nsac

tions

Blo

od p

ress

ure,

SPO

2,U

nobs

trus

ive

Con

figu

ratio

n w

asT

he s

tudy

33 v

olun

teer

s

et a

l. [2

7]on

Inf

orm

atio

n1/

12-l

ead

EC

G, 2

-axi

sw

rist

-wor

nba

sed

on p

atie

ntin

corp

orat

ed th

epa

rtic

ipat

ed in

Tech

nolo

gy in

acce

lero

met

er, p

ulse

,m

ultip

aram

etri

csp

ecif

ic v

alue

s: n

onpa

tient

’s p

rofi

lea

70-m

in te

st.

Bio

med

icin

e.he

art r

ate,

tem

pera

ture

mon

itori

ng

aero

bic/

aero

bic

stat

ein

ord

er to

red

uce

Res

ults

:

(Sen

sors

).sy

stem

.(c

orre

spon

ding

fa

lse

alar

ms.

For

BP,

85%

had

Dat

a w

as e

ncry

pted

usi

ngto

leve

l of

user

It m

easu

red

a di

ffer

ence

of

less

tech

niqu

es in

here

nt in

activ

ity),

age

,pu

lse

and

SPO

2th

an 5

bea

ts w

hen

GSM

/GPR

S.ge

nder

, fitn

ess

and

cont

inuo

usly

. BP

com

pare

d to

sta

ndar

d

Alg

orith

m:

med

ical

his

tory

.an

d 30

s o

f E

CG

inst

rum

ents

.

Pan-

Tom

pkin

s.w

ere

mea

sure

d T

he E

CG

res

ults

3 tim

es a

day

or

wer

e po

or a

s a

resu

lt

at r

eque

st o

f us

er.

of n

oise

but

oth

er

The

use

r’s

read

ings

wer

e ok

.

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

heal

th c

ondi

tion/

70%

of

user

s fo

und

equi

pmen

t sta

tehe

sys

tem

com

fort

able

.

was

gro

uped

into

the

follo

win

g

zone

s ba

sed

on

the

resu

lts:

(i)

norm

al,

(ii)

dev

iant

,

(iii)

ris

k, (

iv)

high

risk

, and

(v)

syst

em e

rror

16.

Chu

ng

2008

Tele

med

icin

eM

icro

phon

e-an

A p

orta

ble

Con

figu

ratio

n w

as5

regu

lar

snor

ers

The

re w

as a

pos

itive

et a

l. [2

8]an

d e-

Hea

lth.

omni

dire

ctio

nal e

lect

rets

tele

mon

itori

ngba

sed

on th

ean

d 5

OSA

Spr

edic

tivity

of

94%

cond

ense

r-ty

pe (

Sens

or).

devi

ce to

dete

ctio

n of

apa

tient

s te

sted

and

a sn

orin

g

Syst

em u

sed

the

mea

sure

dre

cogn

ize

slee

p-sn

orin

g pa

ttern

.th

e sy

stem

.se

nsiti

vity

of

94%

.

sign

als

for

the

virt

ual

rela

ted

brea

thin

gT

he d

etec

tion

The

res

ults

indi

cate

d

sens

ing

of O

bstr

uctiv

edi

sord

ers

intr

igge

red

a sy

stem

an O

SAS

posi

tive

Slee

p A

pnea

Syn

drom

ere

al-t

ime

conf

igur

atio

n th

atpr

edic

tivity

and

(OSA

S).

stim

ulat

ed th

ese

nsiti

vity

of

73.3

%

Syst

em s

uppo

rted

bot

hpa

tient

’s n

erve

inan

d 81

.1%

res

pect

ivel

y.

mon

itori

ng a

nd th

erap

eutic

orde

r to

sto

p th

e

appl

icat

ions

. The

snor

ing.

ther

apeu

tic a

pplic

atio

n

invo

lved

ner

ve s

timul

atio

n

usin

g a

low

fre

quen

cy

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

tran

scut

aneo

us

elec

tric

al s

igna

l.

The

sys

tem

incl

uded

a lo

cal w

ebse

rver

.

Alg

orith

m:

Abs

olut

e di

ffer

ence

betw

een

inpu

t vol

tage

and

base

line

and

mov

ing

aver

age

filte

r w

ith a

win

dow

siz

e of

20.

17.

Chu

n 20

05St

udie

s in

Hea

lthN

on-I

nvas

ive

Blo

odPe

rson

al W

eara

ble

Syst

em w

asT

he p

rim

ary

Sim

ul-T

ests

R

esul

ts

et a

l. [2

9]Te

chno

logy

and

Pres

sure

[N

IBP]

, SPO

2,W

rist

wor

nco

nfig

ured

bas

edsy

stem

was

bas

edat

ion

Info

rmat

ics.

1-ch

anne

l EC

G te

xtro

deIn

tegr

ated

on p

atie

nt’s

on th

e W

IHM

D.

NIB

P10

0±4

mm

g

elec

trod

e, R

espi

rato

ryH

ealth

Mon

itori

ngdi

abet

ic h

isto

ryT

he s

econ

dary

SPO

210

0±2

%

Rat

e [R

R],

Hea

rt R

ate

Dev

ice

(WIH

MD

).an

d po

stpr

andi

alsy

stem

had

aH

R10

0±0

.9%

[HR

], B

ody

surf

ace

time.

num

ber

of n

onR

R50

±1.8

%

tem

pera

ture

,T

he B

P te

sts

also

intr

usiv

e se

nsor

spe

ople

Acc

eler

omet

er, P

ostu

reco

nsid

ered

the

used

on

the

bed,

Tem

p*20

±1.5

(Sen

sors

).pa

tient

’s h

isto

ry.

toile

t sea

t and

Fall#

150

91.3

%#

RR

and

HR

wer

e vi

rtua

lch

air.

Cap

aciti

ve*T

este

d in

tem

pera

ture

sens

ors

base

d on

sens

ors

wer

e us

edco

ntro

lled

cham

ber

EC

G s

igna

ls.

for

the

chai

r.#D

etec

tion

rate

Loc

al-M

VI

10 v

olun

teer

sfo

r si

mul

ated

fal

ls

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

com

mun

icat

ion:

Wir

eles

ste

sted

the

syst

em

Rem

ote-

MV

Ian

d au

thor

s al

so

com

mun

icat

ion:

used

sim

ulat

ions

.

sms,

cel

lula

r.

The

exp

ande

d sy

stem

(Int

egra

ted

Hom

e Te

leca

re

Syst

em)

com

pris

ed th

e

follo

win

g se

nsor

s:

12-c

hann

el E

CG

,

Res

pira

tory

fun

ctio

n,

Blo

od g

luco

se, N

IBP,

Bod

y fa

t met

er

and

Spir

omet

er.

18.

Yu

et a

l. [3

0]20

13Te

lem

edic

ine

Tem

pera

ture

, mob

ileM

obile

pho

neE

ye te

sts

wer

eT

he e

ntir

e te

st

and

e-H

ealth

.ph

one

cam

era

and

base

d sy

stem

base

d on

imag

eto

ok ju

st a

bout

mic

roph

one

(Sen

sors

).fo

r co

nven

ient

size

s on

pho

ne a

nd28

min

utes

.

Syst

em u

sed

a m

obile

“ann

ual

feed

back

fro

m

phon

e as

a m

inia

turi

zed

phys

ical

exa

m”.

user

s.

heal

th e

xam

tool

kit.

The

sys

tem

was

Ele

ctro

nic

heal

th r

ecor

dstr

aine

d as

fol

low

s:

wer

e se

nt to

rem

ote

3 de

ep b

reat

hs

serv

er a

s e-

mai

ls o

r(f

or b

read

th s

ound

),

mm

s m

essa

ges.

1 m

inut

e da

ta

Loc

al-M

VI

(for

hea

rt s

ound

,

com

mun

icat

ion:

Wir

eles

s.te

mpe

ratu

re

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

Rem

ote-

MV

Ian

d E

CG

).

com

mun

icat

ion:

Cel

lula

r.T

here

wer

e 11

Alg

orith

ms:

volu

ntee

rs

Mac

hine

lear

ning

.fo

r th

e te

st.

Syst

em c

arri

ed o

ut te

sts

for

abou

t 12

para

met

ers

used

in a

nnua

l

phys

ical

exa

ms.

19.

Fong

and

20

13Se

nsor

s (B

asel

)Pl

atfo

rm: A

ndro

idC

loud

-bas

edT

he s

yste

mT

he m

easu

rem

ents

The

res

ults

mea

sure

d

Chu

ng[3

1]ba

sed

phon

e.no

n-co

ntac

t EC

Gsu

ppor

ted

are

quir

ed th

eth

e w

ebsi

te lo

adin

g

EC

G, H

eart

Rat

e,m

onito

ring

and

min

imal

leve

l of

patie

nt to

sea

ttim

es a

s fo

llow

s:

Cam

era

(Sen

sors

).a

QR

cod

e ba

sed

adap

tatio

n ba

sed

on a

cha

ir w

ith1s

t vie

w lo

ad ti

me

Dat

a w

as s

hare

d ov

er th

epa

tient

adh

eren

ceon

QR

cod

e.ca

paci

tive

sens

ors.

(2.8

33 m

s), r

epea

t

Inte

rnet

inst

anta

neou

sly

sche

me.

view

tim

e (0

.124

ms)

,

usin

g th

e em

bedd

edth

e D

ocum

ent

web

serv

er.

Com

plet

e pa

ram

eter

,

Loc

al-M

VI

whi

ch o

ccur

s af

ter

all

com

mun

icat

ion:

Blu

etoo

thth

e im

ages

con

tent

com

mun

icat

ion

betw

een

have

bee

n lo

aded

capa

citiv

e co

uple

d(2

.833

s),

the

fully

EC

G s

enso

r an

d ph

one.

load

ed p

aram

eter

,

Inte

r-M

VI

whi

ch in

clud

es a

ny

com

mun

icat

ion:

WiF

i.ac

tivity

trig

gere

d by

HR

was

cal

cula

ted

the

Java

Scri

pt

usin

g a

virt

ual s

enso

r(6

.452

s).

base

d on

EC

G.

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

20.

Gia

nsan

ti 20

13Te

lem

edic

ine

Sens

ors:

Gas

troc

nem

ius

Rea

l-tim

e si

mpl

eT

he p

orta

ble

kit

Syst

em h

as a

hig

h

et a

l. [3

2]an

d e-

Hea

lth.

Exp

ansi

on M

easu

rem

ent

port

able

kit

for

wor

ks w

ith a

leve

l of

accu

racy

Uni

t (G

EM

U),

gyr

osco

pe,

hom

e-ba

sed

casc

ade

ofan

d co

sts

948

EU

R.

acce

lero

met

er, S

EC

OSP

gait

anal

ysis

.In

stru

men

ted

(a s

tep

coun

ter)

.W

alkw

ays

Plat

form

: Cus

tom

(IW

s). I

t als

o us

es

port

able

dev

ice.

wal

king

aid

s.

16 s

ubje

cts

test

ed th

e

syst

em.

21.

Gom

ez

2008

IEE

E T

rans

actio

nsR

ealti

me

Con

tinuo

usT

he P

DA

was

CG

M w

ith r

eal-

Onl

y co

ntro

l

et a

l. [3

3]on

Inf

orm

atio

nG

luco

se M

onito

ring

rem

otel

ytim

e pr

ogra

mm

able

stra

tegi

es #

1 an

d

Tech

nolo

gy in

(CG

M)

sens

or.

prog

ram

med

by

the

insu

lin p

umps

.#2

wer

e te

sted

and

Bio

med

icin

e.Pl

atfo

rm: P

DA

TC

MS

unde

r th

e4

syst

em c

ontr

olth

e m

easu

red

HbA

1c

(iPA

Q h

p221

0).

doct

or’s

sup

ervi

sion

.st

rate

gies

:va

lues

con

firm

ed

Loc

al M

VI:

Blu

etoo

th,

The

insu

lin p

ump

(i)

patie

nt c

ontr

ol:

the

effe

ctiv

enes

s

Infr

ared

or

seri

alw

as c

onfi

gure

dm

anua

l cha

nge

of th

e st

rate

gy.

com

mun

icat

ion.

and

cont

rolle

dsu

perv

ised

Syst

em c

ost:

7,34

8

Rem

ote

MV

I: M

obile

rem

otel

y in

by d

octo

rE

UR

(co

mpa

red

to

GPR

S fo

rre

spon

se to

the

(ii)

doc

tor

cont

rol:

5,90

7 E

UR

for

a C

GM

com

mun

icat

ion

to th

em

easu

red

CG

Mco

me

assy

stem

bas

ed o

n a

Tele

med

icin

e C

entr

alva

lues

.su

gges

tions

that

man

ual a

ppro

ach)

.

Serv

er (

TM

CS)

.th

e pa

tient

sho

uld

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

The

sys

tem

sup

port

s a

dow

nloa

d an

d

hera

peut

ic a

pplic

atio

n-ap

prov

e

the

cont

rol o

f an

(iii)

rem

ote

loop

insu

lin p

ump.

cont

rol:

prog

ram

med

by th

e T

CM

S

unde

r th

e do

ctor

’s

supe

rvis

ion.

(iv)

per

sona

l loo

p

cont

rol:

real

-tim

e

cont

rol o

f th

e

insu

lin p

ump

base

d on

glu

cose

sens

or d

ata

The

pat

ient

has

to

issu

e e-

cons

ents

(dig

itally

sig

ned

cert

ific

ates

) be

fore

pers

onal

dat

a ca

n

be a

cces

sed.

Feas

ibili

ty te

stin

g

phas

e: 4

Typ

e 1

diab

etic

pat

ient

s

for

6 m

onth

s.

Clin

ical

test

ing

phas

e: 1

0 Ty

pe 1

patie

nts

for

8 w

eeks

.

Con

tinu

ed

AP

PE

ND

IX A

. SU

MM

AR

Y O

F ST

UD

IES

INC

LU

DE

D I

N T

HE

RE

VIE

W

S/N

AU

TH

OR

SY

EA

RJO

UR

NA

LA

RC

HIT

EC

TU

RE

AP

PL

ICA

TIO

NA

DA

PT

AT

ION

/

PE

RSO

NA

LIZ

-ST

UD

Y D

ESI

GN

OU

TC

OM

E/

AT

ION

RE

SULT

S

22.

D’A

rcy

2011

IEE

E T

rans

actio

ns o

nE

EG

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MM

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LU

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IX A

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MM

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Y O

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LU

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PE

ND

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MM

AR

Y O

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LU

DE

D I

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ased

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exp

erim

ent

on F

ast F

ouri

erla

sted

for

30

min

s.

Tra

nsfo

rm (

FFT

).

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