In this paper, a novel approach of a human motion classification system in wireless body area network (WBAN) using received radio signal strength was developed. This method enables us to classify human motions in WBAN using only the radio signal strength during communication without additional tools such as an accelerometer. The proposed human motion classification system has a potential to be used for improving communication quality in WBAN as well as recording daily-life activities for self-awareness tool. To construct the classification system, a numerical simulation was used to generate WBAN propagation channel in various motions at frequency band of 403.5MHz and 2.45GHz. In the classification system, a feature vector representing a characteristic of human motions was computed from time-series received signal levels. The proposed human motion classification using the radio signal strength based on WBAN simulation can classify 3-5 human motions with the accuracy rate of 63.8-95.7 percent, and it can classify the human motions regardless of frequency band. In order to confirm that the human motion classification using radio signal strength can be used in practice, the applicability of the classification system was evaluated by WBAN measurement data.

Communication reliability and energy efficiency are important issues that have to be carefully considered in WBAN design. Due to the large path loss variation of the WBAN channel, transmission power control, which adaptively adjusts the radio transmit power to suit the channel condition, is considered in this paper. Human motion is one of the dominant factors that affect the channel characteristics in WBAN. Therefore, this paper introduces motion-aware temporal correlation model-based transmission power control that combines human motion classification and transmission power control to provide an effective approach to realizing reliable and energy-efficient WBAN communication. The human motion classification adopted in this study uses only the received signal strength to identify the human motion; no additional tool is required. The knowledge of human motion is then used to accurately estimate the channel condition and suitably select the transmit power. A performance evaluation shows that the proposed method works well both in the low and high WBAN network loads. Compared to using the fixed Tx power of -5dBm, the proposed method had similar packet loss rate but 20-28 and 27-33 percent lower average energy consumption for the low network traffic and high network traffic cases, respectively.