Subject-adaptive Loose-fitting Smart Garment Platform for Human Activity Recognition

Abstract

The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.