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Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial
Ben Sawaryn1, Michel Klaassen2, Bert-Jan van Beijnum1
1Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
Sensors (Basel, Switzerland)
|September 10, 2021
Summary
This study shows that accelerometers can accurately identify postures and movements for artificial pancreas systems. This could improve glucose control for type 1 diabetes mellitus patients by sensing physical activity.
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Diabetes Management
Background:
- Artificial pancreas (AP™) systems regulate blood glucose in type 1 diabetes mellitus.
- Current AP™ systems lack posture and movement classification for metabolic energy estimation.
- Improvements are possible by integrating physical activity sensing.
Purpose of the Study:
- Investigate using AP™ hardware to identify postures and movements.
- Assess the feasibility of a computationally sparing algorithm for activity recognition.
- Enhance AP™ functionality for more accurate hormone administration.
Main Methods:
- Seven healthy participants performed sequences of postures and movements.
- Accelerometers were placed on the hip, abdomen, sternum, and upper leg.
- User-specific models were trained and assessed using collected sensor data.
Main Results:
- Classification accuracies reached 86.5% with hip sensor alone.
- Including abdomen sensors improved accuracy to 87.3%.
- Adding sternum and upper leg sensors yielded 90.0% accuracy in identifying postures and movements.
Conclusions:
- The existing AP™ hardware is suitable for posture and movement identification.
- The developed algorithm shows potential for improving AP™ by sensing physical activity.
- Further research in daily life settings is needed to confirm accuracy and efficacy.
