Kieran Woodward
ABSTRACT
With the ever increasing elderly population, human activity trackers can help monitor the daily physical activities performed by the elderly in order to contribute towards improvements in independent living and quality of life.
Little activity recognition research has explored the use of multiple Inertial Measurement Units (IMU) to capture both simple and complex human activities. Therefore, existing research may not benefit the monitoring of the elderly population wherein the complexity in the details of changes are not captured. This work proposes a multi-sensor approach measuring acceleration and quaternion values to recognise both simple and complex daily living activities using a deep learning approach. We compare and evaluate the performance of using 1, 3 and 5 on-body IMU sensors to train CNN and LSTM networks with both acceleration and quaternion values. The results show that the adoption of the quaternion values from 5 on-body sensors using the LSTM model outperforms all other models (F1-score=0.9606). This high performance provides many opportunities for the accurate monitoring of complex daily living activities.
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Index Terms
- A multi-sensor deep learning approach for complex daily living activity recognition
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