This paper presents a real-time body motion classification system using the radio channel characteristics of a wearable body area network (BAN). We developed a custom wearable BAN radio channel measurement system by modifying an off-the-shelf ZigBee-based sensor network system, where the link quality indicator (LQI) values of the wireless links between the coordinator and four sensor nodes can be measured. After interpolating and standardizing the raw data samples in a pre-processing stage, the time-domain features are calculated, and the body motion is classified by a decision-tree based random forest machine learning algorithm which is most suitable for real-time processing. The features were carefully chosen to exclude those that exhibit the same tendency based on the mean and variance of the features to avoid overfitting. The measurements demonstrated successful real-time body motion classification and revealed the potential for practical use in various daily-life applications.

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.