This paper describes an online human physical activity (PA) recognition system based on machine learning, using rechargeable wireless wearable sensors with body-energy harvesting. The entire system is introduced and described, including the wireless wearable sensor network with a control center (smartphone), a body-energy harvesting module as the power supply for the sensors, and a PA recognition model based on the random forest algorithm. Hardware design, software design, and algorithm design are described in detail. For the hardware design, there are two main parts: the low-power wireless wearable sensors using Bluetooth 4.0 BLE, and the energy harvesting module. Two experiments were carried out to validate the design. One was an energy harvesting and consumption experiment on the wearable sensors, which determined that harvesting human body energy to supply the wearable sensors is feasible. The other was online human PA recognition based on the rechargeable wearable sensors. According to the results, the body-energy harvesting module has almost no influence on the recognition accuracy, and using the human body movement energy harvesting system for online human PA recognition is practical.