On the combined use of Electromyogram and Accelerometer in Lower Limb Motion Recognition

Abstract

Analysis of motion of lower limbs is required in different fields including health monitoring, robotics, rehabilitation sciences, biometrics and consumer electronics. Motion sensors, such as accelerometers are prominently used in such analysis since they are non-invasive and are readily available in low cost. However, it is evident from literature that fusion of accelerometer data with those recorded from other types of sensors improves the recognition of human activities. In this paper, the use of surface electromyogram (sEMG) along with accelerometers is explored to recognize nine activities of daily living. The effect of the placement of the sEMG sensor on two of the most popularly reported muscle locations on leg, namely soleus and tibialis anterior, is studied in more detail to determine the appropriate positioning of such sensors for human activity recognition and hence, reduce the number of sensors that are required for classification. It is demonstrated using actual data that the use of sEMG along with accelerometer improves the overall classification accuracy to 98.2% from around 94.5%, which is obtained if only accelerometer is used. In particular, the classification of stationary activities is improved with the inclusion of sEMG. Moreover, the placement of the sEMG sensor on soleus muscle aids the classification more as compared to tibialis anterior muscle.