Influence of a loose-fitting sensing garment on posture recognition in rehabilitation

Several smart sensing garments have been proposed for postural and movement rehabilitation. Existing systems require a tight-fitting of the garment at body segments and precise sensor positioning. In this work, we analyzed errors of a loose-fitting sensing garment on the automatic recognition of 21 postures, relevant in shoulder and elbow-rehabilitation. The recognition performance of garment-attached acceleration sensors and additional skin-attached references was compared to discuss challenges in a garment-based classification of postures. The analysis was done with one fixed-size shirt worn by seven participants of varying body proportions. The classification accuracy using data from garment-integrated sensors was on average 13% lower compared to that of skin-attached reference sensors. This relation remained constant even after selecting an optimal input feature set. For garment-attached sensors, we observed that the loss in classification accuracy decreased, if the body dimension increased. Moreover, the alignment error of individual postures was analyzed, to identify movements and postures that are particularly affected by garment fitting aspects. Contrarily, we showed that 14 of the 21 rehabilitation-relevant postures result in a low sensor alignment error. We believe that these results indicate critical design aspects for the deployment of comfortable garments in movement rehabilitation and should be considered in garment and posture selection.