Human Activity Recognition Based on the Fading Characteristics of the On-Body Channel

Several solutions using wearable devices for continuous Human Activity Recognition (HAR) have been proposed up to now, and HAR sensor-based methods are by far the most adopted. However, many data transmission obstacles prevent the viable operation of these systems, such as those related to deep channel fading, the shadowing of body links during movements, packet loss resulting from unpredictable and dynamic body movements, and environmental variations. In this work, a novel HAR approach based solely on on-body Received Signal Strength Indicator (RSSI) measurements of different body channels is presented to discriminate various human activities. In this perspective, a novel hierarchical threshold-based algorithm is proposed to classify RSSI data into two classes. The first one encompasses static movements. The second one groups dynamic movements. In this paper, the features provided to machine learning algorithms are unconventionnaly based on the statistical characterization parameters of the body channel in an indoor environment. These parameters include the average of the slow fading channel component and the fast fading component's cumulative distribution function (CDF). In addition, the impact of the sliding window size adopted for the extraction of the slow fading component is investigated, as well as the impingement of the placement of the radio nodes on the body for the selection of the best on-body link for the transmission. It is shown that the proposed scheme provides higher accuracy compared to the state of the art radio-based HAR techniques and that the mislabeling problem is reduced by distinguishing between static and dynamic activities. Indeed, an approximate F1 score-based accuracy of 99% is obtained with a demeaning window size of 50 ms, considering the most informative channel.