Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks. Designing reliable, scalable and energy efficient sensor-based activity recognition system remains a challenge. Here, the authors demonstrate low power wearable wireless network system based on magnetic induction which is integrated with deep recurrent neural networks for human activity recognition.