Passive indoor visible light-based fall detection using neural networks

In this paper, a passive visible light sensing (VLS) fall detection system based on luminaires is proposed that uses neural networks to learn the state (i.e., upright or prone) of a target (e.g., a person). The proposed method measures the channel impulse response (CIR) between different source-receiver pairs in a passive scenario, where the user does not hold a device or sensor. The CIR measurements are collected in a realistically modeled room and neural networks are employed to learn the relationship between the CIR measurements and the states of the target at randomly selected positions in the room. The performance evaluation of the system shows that an accuracy of more than 97% is attainable by utilizing a large number of data samples and high brightness factor of the luminaires. The robustness of the proposed method is validated by using a tilted state which is labeled with same class as the upright state, however, the tilted state is not used to train the network. One of the key applications of fall detection is in healthcare domain for patient monitoring. The correct prediction of the prone state is particularly critical in such scenarios since emergency situations may arise from a fall.