Spinal Cord Injury CenterBalgrist University HospitalMedical FacultyUniversity of Zurichwww.sci-research.uzh.ch

Sensory-Motor Systems LabInstitute for Robotics and Intelligent SystemsETH Zurichwww.sms.hest.ethz.ch

Automated Stand-Up and Sit-Down Detection for Robot-Assisted Body Weight Support Training with the FLOAT

Regaining gait capabilities requires extensive locomotor training of many everyday activites.1,2

Robotic devices can assist therapists by providing the necessary task-specific support and creating a safe and permissive environment.3

Automatic selection of correct support modes requires fast and robust detection of movement onsets.4

Motivation

We recorded these activities with a Vicon system (Oxford, England) and reflective markers attached to seven anatomical landmarks (Figure 1).

Five able-bodied participants (age: 24-33 years) performed 4 different activities (sitting, standing, stand up, sit down) 3-5 times.

From the recordings, we extracted position and acceleration data in the sagittal plane as classification features.

We trained a linear discriminant analysis classifier for each marker position and different window sizes.

We evaluated classifier performance by calculating movement onset detection times and F1 score defined as:

We applied a leave-one-subject-out cross validation paradigm to evaluate the classifier's robustness.

time (s)

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TrainingValidation

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Methods

M. Bannwart1),2), D. Ernst1), C. Easthope2), M. Bolliger2), G. Rauter1),2),3)

BIROMED-LabDepartment of Biomedical EngineeringUniversity of Baseldbe.unibas.ch/biromed

1) 2) 3)

Investigate optimal sensor location for fast and robust movement onset detection for sit-to-stand and back-to-sit motion.

Head

T10ShoulderClavicle

SternumPosterior Pelvis

Knee

The FLOAT

Figure 1. Participant with marker setup for recording of activities (labelled markers were selected for classification) and body weight support system the FLOAT in the background.

Integration of automatic selection of the appropriate support mode into trainings with The FLOAT.

Goal

1 S. Harkema et al., Evidence based therapy for recovery of function after spinal cord injury, Handbook of Clinical Neurology, Vol. 109, 2012 2 K. Musselman et al., Spinal cord injury functional ambulation profile: A new measure of walking ability, Neurorehabilitation and Neural Repair, Vol 25 (3), 2011 3 M. Wirz, R. Rupp, Application Issues for Robotics, In: Reinkensmeyer D., Dietz V. (eds) Neurorehabilitation Technology, 2016 4 D. Novak et al., Automated detection of gait initiation and termination using wearable sensors, Medical Engineering & Physics , Vol. 35 (12), 2013

References

For fast sit-to-stand detection best single marker location was on the back close to the tenth vertebra (~56 ms). For back-to-sit the hip marker outperformed other anatomical landmarks (~48 ms).

Window sizes around 15 to 35 milliseconds yielded shortest detection times (Figure 2).

Classifier performance for steady-state postures improved with increasing window sizes while for postural transitions large window sizes decreased performance (Figure 3).

Results

Figure 2. Effect of marker locations and window sizes on movement onset detection times for sit-to-stand (left) and back-to-sit (right).

0 5 15 35 75 155 315 635 12750

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Sit-to-stand

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Back-to-sit

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Figure 3. Effect of window sizes on classifier performance (measured by the F1 score).

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Standing

HeadShoulderT10ClavicleSternumPost. PelvisKnee

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Sit-to-stand

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window size (ms) window size (ms) window size (ms) window size (ms)

Discussion & Conclusion

Fast, real-time movement detection to e.g. assist patient motion can be improved with optimal sensor placement and appropriate window sizes.

A single, optimal sensor location for stand up and sit down motion does not exist. The sternum location offers a possible trade-off.

mathias.bannwart@hest.ethz.ch

+41 44 510 72 15