  Recent advancements in Artificial Neural Networks have significantly improved
human activity recognition using multiple time-series sensors. While employing
numerous sensors with high-frequency sampling rates usually improves the
results, it often leads to data inefficiency and unnecessary expansion of the
ANN, posing a challenge for their practical deployment on edge devices.
Addressing these issues, our work introduces a pragmatic framework for
data-efficient utilization in HAR tasks, considering the optimization of both
sensor modalities and sampling rate simultaneously. Central to our approach are
the designed trainable parameters, termed 'Weight Scores,' which assess the
significance of each sensor modality and sampling rate during the training
phase. These scores guide the sensor modalities and sampling rate selection.
The pruning method allows users to make a trade-off between computational
budgets and performance by selecting the sensor modalities and sampling rates
according to the weight score ranking. We tested our framework's effectiveness
in optimizing sensor modality and sampling rate selection using three public
HAR benchmark datasets. The results show that the sensor and sampling rate
combination selected via CoSS achieves similar classification performance to
configurations using the highest sampling rate with all sensors but at a
reduced hardware cost.
