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Upper limb rehabilitation requires long-term, repetitive rehabilitation training and assessment. However, many patients cannot afford for heavy medical fees. It is necessary to design an effective, low-cost, and reasonable home rehabilitation and evaluation system. In this paper, we developed a novel home-based multi-scene upper limb rehabilitation training and evaluation system for post-stroke patients. Based on the Kinect sensor and the posture sensor, the multi-sensors fusion method was used to track the motion of the patients. Multiple virtual scenes were designed to encourage rehabilitation training of upper limbs and trunk. A rehabilitation evaluation method was proposed integrating Fugl-Meyer assessment (FMA) scale and upper limb reachable workspace relative surface area (RSA). Furthermore, an FMA-RSA assessment model was established to assess an upper limb motor function. Correlation-based dynamic time warping was used to solve the problem of inconsistent upper limb movement path in different patients. Two clinical trials were conducted. The experimental results show that the system is very friendly to the subjects. The rehabilitation assessment results by this system are highly correlated with the therapist's (the highest forecast accuracy was 92.7% in the 13th item). It also reveals that long-term rehabilitation training can improve the upper limb motor function of the patients statistically significant (p=0.02 <; 0.05). The system has the potential to become an effective home rehabilitation training and evaluation system.