LAUSR

Wearable motion sensing in daily life has attracted attention in various disciplines. Especially, stretchable strain sensors have been instrumented into garments (e.g. brace). To estimate joint motions from such sensors, previous studies have modelled relationships between the sensor strains and motion parameters via supervised/semi-supervised learning. However, typically these only model a single relationship assuming the sensor to be located at a specific point on the body. Consequently, they exhibit reduced performance when the strain-parameter relationship varies due to sensor shifts caused by long-term wearing or donning/doffing of braces. This letter presents a shift-adaptive estimation of knee joint angle. First, a brace is instrumented with two stretch sensors placed at different heights. Next, the different strain-angle relationships at varying brace shift positions are learned using Gaussian mixture models (GMMs). The system then estimates the joint angle from the sensor strains through Gaussian mixture regression using a maximum likelihood shift GMM, which is identified by referring to the two strains in a previous 1 s period. Experimental results indicated that the proposed method estimates the joint angle at multiple shift positions (0–20 mm) with higher accuracy than methods using a single model, single sensor, or referring to the present sensor strains.