Vibration-based contactless technologies are arising for indoor person location and gait activity recognition applications, which can overcome issues such as privacy breaches and wearing discomfort with traditional methods to satisfy the critical need for health and safety monitoring in smart home. However, simultaneous location and recognition of occupant gait activity using information-starved vibration signals remains challenging due to its less characterization of event details. In nature, scorpions use a 3/1 neuron coding pattern to enrich vibration information by enhancing sensory differences from eight vibration sensilla embedded in their metatarsal ends of each walking leg, thus exhibiting high accuracy in locating moving prey. Inspired by this, we exploit a coding pattern to make vibration signal features richer for discrimination, while designing a bionic gait activity location and recognition system in which the data acquisition device is built by imitating scorpion vibration sensilla array distribution. To verify the feasibility of the system, we collect vibration signals produced by different people with varying gait activities (walking, jogging, running, and stepping) in an actual scene, and a trained hidden Markov model is employed to identify the activity of the current location target. The average estimated error and the relative estimated error of position estimation are calculated to be 0.3517 m and 8.79 %, respectively. The average recall and average precision of gait activity recognition are 95.83 % and 95.84 %, respectively. Our system is proven to simultaneously locate and recognize occupant gait activities, which can meet the needs including smart home, and health care monitoring.