Detecting activities of daily living (ADL) and classifying the gesture typology are important tasks for rehabilitation and for applications in robotics. The use of wearable sensors, such as accelerometers, could facilitate the previous tasks since it would open the possibility of monitoring patients in real-life conditions. This study aims at detecting and classifying gestures recorded by accelerometers: in particular, a set of upper limb motor tasks that are contained in the rehabilitation scale known as Wolf Motor Function Test (WMFT), were used in this work. Two accelerometers, respectively a dual-axis one placed on the biceps and a three-axis one placed internally on the wrist, constitute the sensors set. Five normal subjects were included in the protocol and were asked to perform five different gestures. Dynamic Time Warping (DTW) approach was chosen to process data: this is a template matching technique that assesses the similarity between signals by using a reference signal (i.e. the Template). In this work a novel approach for the construction of the Template is proposed. The Median point DTW Template (MDTW), which is built by connecting the non-linear path between two signals corresponding to movements performed at two different speeds, is introduced. One MDTW was built for each channel, each gesture and each subject, and it was used as a reference signal to recognize five different gestures at various velocities. Moreover, aiming at the generalization of the approach, the recognition performance was also assessed on the classification obtained by using a unique Template for all the five voluntaries for each channel and for each gesture. The recognition percentage obtained by using the subject specific version of MDTW is around 94%, while with the subject independent approach the increase of generalization makes the recognition percentage decrease at around 88%. This latter approach would improve the applicability of wearable monitoring, by substantially decreasing the burden time for the template set construction on a subject-by-subject basis.
Muscillo, R., Schmid, M., Conforto, S. (2008). The median point DTW template to classify upper limb gestures at different speeds. In IFMBE Proceedings (pp.63-66) [10.1007/978-3-540-89208-3_17].
The median point DTW template to classify upper limb gestures at different speeds
MUSCILLO, ROSSANA;SCHMID, Maurizio;CONFORTO, SILVIA
2008-01-01
Abstract
Detecting activities of daily living (ADL) and classifying the gesture typology are important tasks for rehabilitation and for applications in robotics. The use of wearable sensors, such as accelerometers, could facilitate the previous tasks since it would open the possibility of monitoring patients in real-life conditions. This study aims at detecting and classifying gestures recorded by accelerometers: in particular, a set of upper limb motor tasks that are contained in the rehabilitation scale known as Wolf Motor Function Test (WMFT), were used in this work. Two accelerometers, respectively a dual-axis one placed on the biceps and a three-axis one placed internally on the wrist, constitute the sensors set. Five normal subjects were included in the protocol and were asked to perform five different gestures. Dynamic Time Warping (DTW) approach was chosen to process data: this is a template matching technique that assesses the similarity between signals by using a reference signal (i.e. the Template). In this work a novel approach for the construction of the Template is proposed. The Median point DTW Template (MDTW), which is built by connecting the non-linear path between two signals corresponding to movements performed at two different speeds, is introduced. One MDTW was built for each channel, each gesture and each subject, and it was used as a reference signal to recognize five different gestures at various velocities. Moreover, aiming at the generalization of the approach, the recognition performance was also assessed on the classification obtained by using a unique Template for all the five voluntaries for each channel and for each gesture. The recognition percentage obtained by using the subject specific version of MDTW is around 94%, while with the subject independent approach the increase of generalization makes the recognition percentage decrease at around 88%. This latter approach would improve the applicability of wearable monitoring, by substantially decreasing the burden time for the template set construction on a subject-by-subject basis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
