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Deep learning has permitted unprecedented performance in sensor-based human activity recognition (HAR). However, deep learning models often present high computational overheads, which poses challenges to their implementation on resource-constraint devices such as microcontrollers. Usually, the computational overhead increases with the input size. One way to reduce the input size is by constraining the number of sensor channels. We refer to sensor channel as a specific data modality (e.g. accelerometer) placed on a specific body location (e.g. chest). Identifying and removing irrelevant and redundant sensor channels is feasible via exhaustive search only in cases where few candidates exist. In this paper, we propose a smarter and more efficient way to optimize the sensor channel selection during the training of deep neural networks for HAR. Firstly, we propose a light-weight deep neural network architecture that learns to minimize the use of redundant and irrelevant information in the classification task, while achieving high performance. Secondly, we propose a sensor channel selection algorithm that utilizes the knowledge learned by the neural network to rank the sensor channels by their contribution to the classification task. The neural network is then trimmed by removing the sensor channels with the least contribution from the input and pruning the corresponding weights involved in processing them. The pipeline that consists of the above two steps iterates until the optimal set of sensor channels has been found to balance the trade-off between resource consumption and classification performance. Compared with other selection methods in the literature, experiments on 5 public datasets showed that our proposal achieved significantly higher F1-scores at the same time as utilizing from 76% to 93% less memory, with up to 75% faster inference time and as far as 76% lower energy consumption.
|Title of host publication||Proceedings of the 20th International Conference on Information Processing in Sensor Networks, IPSN 2021|
|Number of pages||13|
|Publication status||Published - 18 May 2021|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Information Processing in Sensor Networks - Virtual, Online, United States|
Duration: 18 May 2021 → 21 May 2021
Conference number: 20
|Conference||International Conference on Information Processing in Sensor Networks|
|Period||18/05/2021 → 21/05/2021|
- deep learning
- human activity recognition
- Sensor channel selection
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