![]() To evaluate the performance of the proposed system, a sensor system prototype using long short-term memory was constructed, and experiments with 25 participants were performed. ![]() Distance changes measured by ultrasonic sensors are used as time-independent patterns to recognize when an elderly person falls. An array of ultrasonic sensors, whose ranges are designed to cover the room space, are initially installed on a wall of the room, and the sensors are rotated to transmit and receive ultrasonic signals to measure the distances to a moving object while preventing ultrasonic signal interference. An ultrasonic-based sensor system for elderly fall monitoring in a smart room is proposed in this study. To reduce the risk of elderly people falling in a private room without relying on a closed-circuit television system that results in serious privacy and trust concerns, a fall monitoring system that protects the privacy and does not monitor a person’s activities is needed. This means that overfitting can be suppressed by increasing the regularization, and thus an appropriate number of hidden layer nodes and a regularization rate must be selected to improve the fall detection efficiency. It is also observed that as the number of hidden layer nodes or sampling interval increases, the regularization rate at the highest value of accuracy increases. In order to resolve the possible overfitting issue, training and validation data are evaluated separately in 4:1, and then in terms of validation data accuracy of the STM-LSTM-based fall detection system, optimal values of the parameters in LSTM and normalization method are found as follows: best accuracy of 98.21% at no-normalization, no-sampling, 128hidden layer nodes, and regularization rate of 0.015. In addition, in the optimized LSTM fall detection system, this may be overfitted because all data are trained without separating any validation data. The training accuracy of the STM-LSTM fall detection system is 100%, while the highest training accuracy by the STM-HMM-based one is 99.5%, which is 0.5% less than the best of the STM-LSTM-based system. In terms of training data accuracy, the proposed STM-LSTM-based fall detection system is compared with the previously reported STM-hidden Markov model (HMM)-based fall detection system. A fall detection system that combines a simple threshold method (STM) and LSTM, the STM-LSTM-based fall detection system, is introduced. The sensed data are processed by the embedded environment and classified by a long-term memory (LSTM). Therefore, falls are detected using a pendant-type sensor that can be worn comfortably for fall detection. In an aging global society, a few complex problems have been occurring due to falls among the increasing elderly population.
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