A Real-Time Patient Monitoring Framework for Fall Detection

Abstract

Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

“…AI paradigms Machine learning [26]; [63]; [67][68][69][70]; Probabilistic methods [70]; [73][74][75]; [77]; [80][81][82]; [87]; [90]; [93,94]; [96][97][98][99][100][101]; [112][113][114]; [134]; [136][137][138] Knowledge-based [26]; [63]; [67][68][69][70][71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139][140][141][142]…”

“…AI systems, in combination with sensors, cameras and other data collection devices, have been developed to monitor the health and wellbeing of individuals [70,87]. Machine learning techniques can be used to improve the cost and efficiency of fall detection devices [99], and detect changes in sleep, mood, heartbeat, respiration, and other vital signs [118,129]. Wearable devices, or 'smart textiles, enabled by AI, can detect changes in the human body and report findings to health care providers [129].…”

Deep Learning Based Systems Developed for Fall Detection: A Review

“…The proposed method lays a foundation for fall protection studies. Ajerla et al [102] proposed a framework for fall detection based on LSTM network that used edge devices like a laptop for computing rather than sending raw data to the cloud for real-time prediction of the fall events. The proposed framework used a cheap MetaMotionR sensor from MbientLab for three-axis accelerometer raw data, a streaming engine Apache Flink which is an open-source software through which data analytics has been streamed.…”

Current state of fall prevention and management policies and procedures in Canadian spinal cord injury rehabilitation

“…These issues may be addressed by educating staff on the importance of tracking falls, reinforcing a "no-blame reporting of incidents", and reducing lengthy reporting processes [34]. Also, the use of wearable devices to detect falls in this population group could be considered as a future direction [35][36][37].…”