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Timo Sztyler
PhD Thesis Defense
Sensor-based Human Activity Recognition:
Overcoming Issues in a Real World Setting
1Timo Sztyler09.05.2019
09.05.2019
2Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Content
1. Motivation
2. What is Activity Recognition?
3. Activity Recognition with Wearable Devices
4. Activity Recognition within Smart Environments
5. Conclusion and Future Work
PHD
THESIS
DEFEN
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Timo Sztyler
MOTIVATION
3Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
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4Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Motivation (Why?)P
HD TH
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Timo Sztyler
Insufficient physical activities but also the absence of needed help can lead to difficult-to-treat long-term effects.
The consequences are ...
… loss of self-confidence
… change in behavior to prevent issues
… physical but also a psychological decline in health
… premature death
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5Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Motivation (How?)P
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Human Activity Recognition has been deeply investigated in the last decade.
many pervasive health care systems have been proposed
knowledge about the performed activities is a fundamental requirement
sensor miniaturization and wireless communications have paved the way
Our goal is to overcome this shortcomings and limitations!
effectiveness out of the lab is still limited
effective in controlled environments
09.05.2019
6Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Activity RecognitionP
HD TH
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Timo Sztyler
Interpreting sensor data or signals to determine the activity which initially triggered them
Sensor types
External sensors
Wearable sensors
motion, proximity, environmental, video, and physiological
carried by the user and are mostly used to recognize simpler activities like motions or postures
intelligent- or smart-homes are typical examples of external sensing and recognize fairly complex activities like taking medicine
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7Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Activity RecognitionP
HD TH
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Physical Activities
Activities of Daily Living (ADL)
refers to people's daily self-care activities
As this suggests, the HAR research area is fragmented …
refers to walking, standing, sitting, running, …
usually recognized by sensors that are attached to certain body parts (wearable sensors)
usually recognized by sensors that are attached to preselected objects or locations (external sensors)
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8Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Activity RecognitionP
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Timo Sztyler
… to recognize the daily routine
Recognizing activities enables …
… to learn the user's behavior
… to optimize the course of the day
… to verify predefined patterns like medical instructions
State-of-the-art human activity recognition systems are far from being able to achieve this
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9Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Activity RecognitionP
HD TH
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Timo Sztyler
Activity Recognition
on-bodyposition
positionaware
cross-subject
person-alization
avoidlabeled datasets
handle diversity
online recogniti
on
person-alization
ACTIVITY RECOGNITION WITH WEARABLE DEVICES
10Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
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11Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Activity Recognition with Wearable DevicesP
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Timo Sztyler
Especially accelerometers were investigated for recognizing physical activities (mainly under laboratory conditions)
the user decides where to carry a wearable device
The step out of the lab leads to new unaddressed problems:
elderly or patients might not be able to collect data
movement patterns of a person could change
We aim to develop robust activity recognition methods that generate high quality results in a real world setting.
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12Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Research QuestionsP
HD TH
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Timo Sztyler
Given a cross-subjects based activity recognition model, how can we adapt the model efficiently to the movement patterns of the user?
Is it possible to recognize automatically the on-body position of a wearable device by the device itself?
RQ1.1
RQ1.2 How does the information about the wearable device on-body position influence the physical activity recognition performance?
RQ1.3 Which technique can be used to build cross-subjects based activity recognition systems?
RQ1.4
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13Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Research Questions (Catchwords)P
HD TH
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… recognizing the on-body position …RQ1.1
RQ1.2 … position-aware physical activity recognition …
09.05.2019
• 15 subjects (8 males / 7 females)
• seven wearable devices / body positions
• chest, forearm, head, shin, thigh, upper arm,waist
• acceleration, GPS, gyroscope, light, magneticfield, and sound level
• climbing stairs up/down, jumping, lying,standing, sitting, running, walking
• each subject performed each activity ≈10 minutes
Data Collection
To address the mentioned problem it was necessary to create a new data set
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Data Collection
• common objects and clothes to attach the devices
• subjects walked through downtown or jogged in a forest.
• each movement was recorded by a video camera
• We recorded for each position and axes 1065 minutes
We focused on realistic conditions
complete, realistic, and transparent data set
15Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
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Feature Extraction
Methods
Time Correlation coefficient (Pearson), entropy (Shannon), gravity (roll, pitch), mean, mean absolute deviation, interquartile range (type R-5), kurtosis, median, standard deviation, variance
Frequency Energy (Fourier, Parseval), entropy (Fourier, Shannon), DC mean (Fourier)
• time and frequency-based features
• gravity-based features (low-pass filter)
• derive device orientation (roll, pitch)
So far, there is no agreed set of features …
… but splitting the recorded data into small overlapping segments has been shown to be the best setting.
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Position Detection
Scenario: Single User
lying, standing, and sitting lead to misclassification
static vs. dynamic activities
gravity provides useful information but …
Stratified sampling and 10-fold cross validation
… it is no indicator of the device position
Broad set of classifiers
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Insights
Setting
Position Detection
• RF outperforms the other classifier (89%)
• The training phase of RF was oneof the fastest
• k-NN (75%), ANN (77%), and SVM (78%) achieved reasonableresults(parameter optimization was performed)
0,00
0,02
0,04
0,06
0,08
0,10
Classifier (PF-Rate)
NB
kNN
ANN
SVM
DT
RF
To compare the results we also evaluated further classifiers
0,35
0,45
0,55
0,65
0,75
0,85
0,95
Classifier (F-Measure)
NB
kNN
ANN
SVM
DT
RF
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Physical Activity Recognition
Feasibility: Used the results of the previous experiment (including all mistakes)
Again, we evaluated two approaches …
• position-independent activity recognition
• position-aware activity recognition
Set of individual classifiers for each position and subject
1) First decide if static or dynamic
2) Apply activity-level depended classifier (different feature sets)
3) Apply position-depended classifier
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Physical Activity Recognition
0,02
0,03
0,04
0,05
0,06
Classifiers
FP-R
ate
NB
kNN
SVM
ANN
DT
RF
0,55
0,60
0,65
0,70
0,75
0,80
0,85
Classifiers
F-m
eas
ure
NB
kNN
SVM
ANN
DT
RF
To compare the results we also evaluated further classifiers
• RF achieved the highest recognition rate (84%)
• All classifier performed worse in a position-independent scenario
RF performed the best in all settings.
• k-NN (70%) and SVM (71%) performed almost equal but worse than ANN (75%) and DT (76%)
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21Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Research Questions (catchwords)P
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Timo Sztyler
… personalization of activity recognition models…
RQ1.3 … cross-subjects based activity recognition …
RQ1.4
09.05.2019
Online Random Forest
Considering online mode, the main differences are …
bagging (generation of subsamples)
growing of the individual trees
replace sample with replacement with Poisson(1)
Select thresholds and features randomly(Extreme Randomized Forest)
Training Sample
Predictionk=Poisson (1)
k=Poisson (1)
. . .Tree #1
Tree #n
. . .
k-times
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Active Learning
Smoothing
classification result
AskUser
aggregate uncertain recognitions
Online Learning
update
updateBody Sensor
Network
Labeled data set for base model
New labeled data set
Updatable Model
Personalization: Online and Active Learning
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Personalization: Online and Active Learning
Smoothing
classification result
Online Learning
update
Updatable Model
Smoothing adjusts the classification result of a single window if it is surrounded by another activity
adjusted window is used to update the model
focuses on minor classification errors
i
i+1
i+2
i-1
i-2
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Personalization: Online and Active Learning
Active Learning
classification result
AskUser
aggregate uncertain recognitions
Online Learning
update
New labeled data set
Updatable Model
User-Feedback queries the user regarding uncertain classification results
infeasible to ask for a specific window (1 sec)
focuses on major classification errors
specified a duration of uncertainty
query result is a new data set
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Personalization (Results)Personalization is a continuous process …
especially dynamic activities improve significantly
most improvement in the first two time intervals
first iteration +4%, five iterations +8%
number of windows with a low confidence value decrease with each iteration
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ParameterConsidering different confidence thresholds …
Considering a different number of trees…
turning point t=0.5
10 questions +8%
10 trees vs. 100 trees
a smaller forest is more feasible concerning wearable devices
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Main Contributions
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• A new real world dataset for on-body position detection and position-aware physical activity recognition
• We show that our on-body position recognition method consistently improves the recognition of physical activities in a real world setting.
• We show that using labeled data of different people of the same gender and with a similar level of fitness and statue is feasible for cross-subjects activity recognition for people that are unable to collect required data.
• We present a physical activity recognition approach that personalize cross-subjects based recognition models by querying the user with a reasonable number of questions.
ACTIVITY RECOGNITION WITHIN SMART ENVIRONMENTS
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30Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Activity Recognition within Smart Environments
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… it says nothing about the actual situation
While the physical activity is a valuable information …
Sensors that are attached to items, furniture, or walls should overcome this problem.
Critical activities (Activities of Daily Living) are not recognized
An ADLs is more diverse than a physical activity.
State of the Art and Open IssuesMost ADL recognition systems rely on …
acquire expensive labeled data set
… supervised-based approaches:
enumerating all possible actions of an ADL
… knowledge-based approaches:
not flexible
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often user/environment-specific
questionable if such models could cover different environments and modes of execution
32Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Research QuestionsP
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Timo Sztyler
Given a generic model of a smart environment, how can it be adapted to a certain environment and user at run-time?
Which method can be used to overcome the requirement of a large expensive labeled dataset of Activities of Daily Living?
RQ2.1
RQ2.2 Which type of recognition method is suitable for handling the diversity and complexity of Activities of Daily Living?
RQ2.3 How can external sensor events be exploited to recognize Activities of Daily Living in almost real-time?
RQ2.4
09.05.2019
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Research Questions (catchwords)P
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Timo Sztyler
… avoid large expensive labeled dataset …RQ2.1
RQ2.2 … method for handling the diversity of ADLs …
09.05.2019
ScenarioRecognizing activities of daily living in a smart-home
to support healthcare, home automation, a more independent life, …
We rely on unobtrusive sensors …
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Our approach …
… overcomes drawbacks of supervised-based approaches
… relies on semantic relations (activities↔ events)
… recognizes interleaved activities
derived from ontological reasoning
inferred by a probabilistic model
not user/environment-specific, no expensive data set, …
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System overview
1.
2.
3.
Semantic correlation reasoner
Semantic integration
layer
Statistical analysis of events
Markov Logic Network (MLN) / MAP Inference
MLN knowledge base
Event(se1,et1,t1)semantic correlations
Recognized activity
instances
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1. Semantic Correlation Reasoner
Why do we use Ontology (OWL2)?
to derive semantic correlations (event type ↔ activity class)
stove silverware_drawer freezer
Hot meal 0.5 0.33 0.5
Cold meal 0.0 0.33 0.5
Tea 0.5 0.33 0.0pre
par
e
interact
{turn on stove} is a predictive sensor event
type for {Prepare hot meal} and {Prepare tea}
OWL2 Reasoner infers
PPM Matrix
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HD TH
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EFENSEOntology
Issues of this approach
Our goal is to refine and improve semantic correlations thanks to collaborative active learning!
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Semantic correlations are computed based on an ontology written by knowledge engineers (humans)
it is very likely that the ontology is incomplete
it is hence questionable if it can cover different environments/mode of execution
2. Statistical Analysis of Events
Input: PPM matrix and temporally ordered events
infers most probable activity class for each event
allows to define activity boundaries(activity instance candidate)
activity instance
candidate
Events
Temporal extension of MLN (MLNNC ) Knowledge Base
Our ontology is translated
into the MLNNC model
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3. MLN / MAP Inference
Hidden predicates
Observed predicates
Event 1: opens freezer (1:00pm)Event 2: turns on stove (1:02pm)
hot meal?cold meal?
tea?
ADL
Sensor EventStove
Hot meal
belong to ADL
0.5: hot meal 0.5: cold meal 0.0: tea
Sensor Event Freezer
&
0.5: hot meal 0.0: cold meal 0.5: tea
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Data SetsWe consider two well-known data sets …
1. CASAS (controlled environment)
2. SmartFABER (uncontrolled environment)
• Interleaved ADLs of twenty-one subjects
• Sensors: movement, water, interaction, door, phone
• Activities: fill medications dispenser, watch DVD, water plants,answer the phone, clean, choose outfit, …
• An elderly woman diagnosed with Mild Cognitive Impairment
• Sensors: magnetic, motion, presence, temperature
• Activities: taking medicines, cooking, …
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CASAS (1/2)
0,6
0,65
0,7
0,75
0,8
0,85
0,9
ac1 ac2 ac3 ac4 ac5 ac6 ac7 ac8
MLNNC (Dataset)
MLNNC (Ontology)
HMM (related work)• Our approach outperforms HMM
ontological reasoning is effective
0
0,5
1
1,5
2
2,5
3
Delta-Start Delta-Dur
F-M
easu
re
Min
ute
s
Candidate
Refined• Refinement improves boundary precision
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SmartFABER (2/2)
0,6
0,65
0,7
0,75
0,8
0,85
0,9
ac9 ac10 ac11
MLNNC (Dataset)
MLNNC (Ontology)
Supervised / SmartFarber
0
5
10
15
20
25
Delta-Start Delta-Dur
Min
ute
s
F-M
easu
re
Candidate
Refined
• unsupervised and supervised-based results are comparable
• results were penalized by a poor choice of sensors
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44Sensor-based Human Activity Recognition:Overcoming Issues in a Real World Setting
Research Questions (catchwords)P
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Timo Sztyler
… personalize model to a user and environment …
RQ2.3 … recognizing ADLs in almost real-time …
RQ2.4
09.05.2019
Architecture (Extension) 3. Collaborative Feedback
Aggregation
Home
Continuous stream of Sensor Events
1. Probabilistic and Ontological
Activity Recognition
2. Query decision(entropy-based)
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2. Query decision
Continuous Stream of Sensor Events
Online rule-based segmentation
Query decision(entropy-based)
Semantic correlations
Segment
Sensor events
Query
Feedback
3. Collaborative Feedback
Aggregation
...
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Online rule-based segmentation
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We consider five aspects …
Object interaction
Change of context
Consistency likelihood
Time leap
Change of location
We introduced two metrics …
Purity of a segment
Number of generated segments (DS)
0,8 0,85 0,9 0,95
CASAS
SmartFABER
Purity (higher is better)
Naive Our Approach
0 10 20 30
CASAS
SmartFABER
DS (lower is better)
Naive Our Approach
3. Collaborative Feedback Aggregation
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Labeled segments are transmitted to a cloud service by the participating homes
it stores feedback items: correspondence between sensor event types and activities
Periodically, a personalized update is transmitted to each home
it contains reliable feedback items provided by similar environments
Semantic Correlation Updater
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Each home receives periodically a set of personalized feedback items
predictiveness is used to provide a semantic correlation to those event types for which the original ontology did not provide a starting correlation
estimated similarity is used to scale semantic correlations of an event type which were originally computed by the ontology
Recognition results (F1 score)
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Entropy threshold vs. number of queries
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Main Contributions
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An unsupervised ADL recognition method that overcomes the main drawbacks of supervised- and specification-based approaches.
A novel online segmentation algorithm that combines probabilistic and symbolic reasoning to divide on the fly a continuous stream of sensor events into high quality segments.
A new active learning approach to Activity of Daily Living recognition that addresses the main problems of current statistical and knowledge-based methods
…
Summary - Activity Recognition
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MotionSensors
Physiological Sensors
ProximitySensors
EnvironmentalSensors
Physical Activities
(Emotional) Conditions
(Usage of)Objects
Location /Weather
Activities of Daily Living
Machine Learning (e.g. Trees, Networks)
Probabilistic Model (e.g. Markov Logic)
Analyzing the Daily Routine
Process Mining (e.g. Conformance Checking)
Publications
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• T. Sztyler and H. Stuckenschmidt, “On-body localization of wearable devices: An investigation of position-aware activity recognition,” in 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE Computer Society, 2016, pp. 1–9, doi: 10.1109/PERCOM.2016.7456521.
• D. Riboni, T. Sztyler, G. Civitarese, and H. Stuckenschmidt, “Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning,” in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2016, pp. 1–12, doi: 10.1145/2971648.2971691.
• T. Sztyler, H. Stuckenschmidt, and W. Petrich, “Position-aware activity recognition with wearable devices,” Pervasive and Mobile Computing, vol. 38, no. Part 2, pp. 281–295, 2017, doi: 10.1016/j.pmcj.2017.01.008.
• T. Sztyler and H. Stuckenschmidt, “Online personalization of cross-subjects based activity recognition models on wearable devices,” in 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE Computer Society, 2017, pp. 180–189, doi: 10.1109/PERCOM.2017.7917864.
Publications
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• T. Sztyler, “Towards real world activity recognition from wearable devices,” in 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE Computer Society, 2017, pp. 97–98, doi: 10.1109/PERCOMW.2017.7917535.
• T. Sztyler, G. Civitarese, and H. Stuckenschmidt, “Modeling and reasoning with Problog: An application in recognizing complex activities,” in 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE Computer Society, 2018, pp. 781–786, doi: 10.1109/PERCOMW.2018.8480299.
• C. Krupitzer, T. Sztyler, J. Edinger, M. Breitbach, H. Stuckenschmidt, and C. Becker, “Hips do lie! A position-aware mobile fall detection system,” in 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE Computer Society, 2018, pp. 95–104, doi: 10.1109/PERCOM.2018.8444583.
• G. Civitarese, C. Bettini, T. Sztyler, D. Riboni, and H. Stuckenschmidt, “NECTAR: Knowledge-based collaborative active learning for activity recognition,” in 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE Computer Society, 2018, pp. 125–134, doi: 10.1109/PERCOM.2018.8444590.
Publications
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• T. Sztyler, J. Carmona, J. Völker, and H. Stuckenschmidt, “Self-Tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data”, Springer-Verlag Berlin Heidelberg, 2016, vol. 9930, pp. 160–180, doi: 10.1007/978-3-662-53401-4.
• T. Sztyler, J. Völker, J. Carmona, O. Meier, and H. Stuckenschmidt, “Discovery of personal processes from labeled sensor data - An application of process mining to personalized health care, ” in Proceedings of the International Workshop on Algorithms & Theories for the Analysis of Event Data, ATAED. CEUR-WS.org, 2015, pp. 31–46. ISSN 1613-0073
• C. Civitarese, G. Bettini, T. Sztyler, D. Riboni, and H. Stuckenschmidt, “newNECTAR: Collaborative active learning for knowledge-based probabilistic activity recognition”, Pervasive and Mobile Computing (2019), vol. 56, pp. 88–105, doi: j.pmcj.2019.04.006
•and more ….
Thank you for your attention :)
…and especially “thank you” to all my friends and co-authors!
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