Development and Evaluation of a Real-Time Home Monitoring Application Utilising Long Short-Term Memory Integrated in a Smartphone

A novel real-time home monitoring application was developed that utilises long short-term memory (LSTM) and is integrated in a smartphone. Its personalised LSTM accurately learns to detect abnormal movement patterns. The application locally processes the smartphone’s accelerometery data in the form of a signal magnitude vector (SMV) to analyse and interpret the movement patterns. The LSTM was conceptualised by a univariate time-series regression model. It adaptively updates its training parameters by processing the individual’s last seven days of movement data, thus providing a stable, individualised, and dynamic activity baseline. It then quantifies the normal and abnormal movement patterns by continuously comparing the learnt information against the current accelerometery readings. An abnormal movement pattern, e.g., a fall or an unexpected period of inactivity triggers multi-channel alerts to care givers using SMS and email. The application’s performance was evaluated using the data collected from 25 adult volunteers, aged 40–70 years. By interpreting their movement patterns, the application demonstrated a detection accuracy quantified by the coefficient of determination (R2) = 0.93 and an absolute error of 0.05. This precision highlighted a low false positive rate in a real-world evaluation. The study successfully demonstrated a robust, cost-effective, and privacy-preserving home monitoring technology.