Human biometric and activities can be acquired from their motions and postures. Conventional video cameras have many limitations. In this dissertation research our goal is to develop sensor hardware as well as machine learning algorithms/software to achieve motion recognition with low communication bandwidth and processing complexity. We have designed the wireless sensing systems targeting the following two applications: (1) Binary compressive sensing (CS) systems for smart home. The binary sensing systems are designed to obtain the geometric information of human motions for the recognition of indoor activities. CS theory is used in the design of sensor sampling structure. We employ Buffon's Needle model of integral geometry to describe human gait changes, and use Hidden Markov Model (HMM) to extract the statistic features for motion recognition. Pyroelectric Infrared (PIR) sensors are used for human gait recognition. Both passive PIR sensor network and active PIR sensors are developed to detect moving and static thermal targets respectively. Laser sensors are used for gait disorder recognition with metrics of symmetry, coordination, and balance. Fiber optic sensors have been deployed and encoded on the ground for multiple human subject location based on Low density parity check (LDPC) codes. (2) Motion capture system for rehabilitation training. Many patients who suffer from the paralysis can recover body functions by taking appropriate rehabilitation training. This study aims to develop a home-oriented cyber-physical system (CPS) to help the patients improve their motion ability via physical training. The system provides quantitative evaluation for the performed motions. The measures evaluated by the system include the motion style of the legs, the periodicity of the foot trajectory, and the foot balance level. The motions of legs and feet are recorded by the thermal camera, and the plantar pressure is measured by the insole pressure sensors. We have developed algorithms to extract the leg skeletons from the thermal images, and to implement motion auto-segmentation, recognition and analysis for the above mentioned measures. This dissertation explores the frontier of intelligent sensing systems for human motion recognition. We have conducted many experiments to demonstrate the efficiency and capability of our methods and systems.