SAMoSA: Sensing Activities with Motion and Subsampled Audio

Despite advances in audio- and motion-based human activity recognition (HAR) systems, a practical, power-efficient, and privacy-sensitive activity recognition system has remained elusive. State-of-the-art activity recognition systems often require power-hungry and privacy-invasive audio data. This is especially challenging for resource-constrained wearables, such as smartwatches. To counter the need for an always-on audio-based activity classification system, we first make use of power and compute-optimized IMUs sampled at 50 Hz to act as a trigger for detecting activity events. Once detected, we use a multimodal deep learning model that augments the motion data with audio data captured on a smartwatch. We subsample this audio to rates <1 kHz, rendering spoken content unintelligible, while also reducing power consumption on mobile devices. Our multimodal deep learning model achieves a recognition accuracy of 92.2% across 26 daily activities in four indoor environments. Our findings show that subsampling audio from 16 kHz down to 1 kHz, in concert with motion data, does not result in a significant drop in inference accuracy. We also analyze the speech content intelligibility and power requirements of audio sampled at less than 1 kHz and demonstrate that our proposed approach can improve the practicality of human activity recognition systems.

Download

Paper PDF

Reference

Vimal Mollyn, Karan Ahuja, Dhruv Verma, Chris Harrison, and Mayank Goel. 2022. SAMoSA: Sensing Activities with Motion and Subsampled Audio. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 3, Article 132 (September 2022), 19 pages. https://doi.org/10.1145/3550284

Chris Harrison	Research Curriculum Vitae Fun Projects Travel
SAMoSA: Sensing Activities with Motion and Subsampled Audio Despite advances in audio- and motion-based human activity recognition (HAR) systems, a practical, power-efficient, and privacy-sensitive activity recognition system has remained elusive. State-of-the-art activity recognition systems often require power-hungry and privacy-invasive audio data. This is especially challenging for resource-constrained wearables, such as smartwatches. To counter the need for an always-on audio-based activity classification system, we first make use of power and compute-optimized IMUs sampled at 50 Hz to act as a trigger for detecting activity events. Once detected, we use a multimodal deep learning model that augments the motion data with audio data captured on a smartwatch. We subsample this audio to rates <1 kHz, rendering spoken content unintelligible, while also reducing power consumption on mobile devices. Our multimodal deep learning model achieves a recognition accuracy of 92.2% across 26 daily activities in four indoor environments. Our findings show that subsampling audio from 16 kHz down to 1 kHz, in concert with motion data, does not result in a significant drop in inference accuracy. We also analyze the speech content intelligibility and power requirements of audio sampled at less than 1 kHz and demonstrate that our proposed approach can improve the practicality of human activity recognition systems. Download Paper PDF Reference Vimal Mollyn, Karan Ahuja, Dhruv Verma, Chris Harrison, and Mayank Goel. 2022. SAMoSA: Sensing Activities with Motion and Subsampled Audio. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 3, Article 132 (September 2022), 19 pages. https://doi.org/10.1145/3550284
© Chris Harrison