Wearable Fall Detection Using Dynamic Communication Rates
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Solution Overview
Problem
Existing fall detection systems for the elderly and sick face challenges in battery life due to continuous processing and reliance on mobile phones, which are not always on the body, limiting effective monitoring and increasing medical costs.
Innovation Solution
A system comprising a wearable device with a fall monitor and a mobile device connected to a server, where the wearable device continuously records movement data in the background, and the mobile device discriminates fall severity based on activity logs, using a selectable communication rate to conserve battery life and improve monitoring efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the fall detection system runs continuously in the foreground of the wearable device processor, then real-time fall detection capability is improved, but battery life deteriorates due to substantial processing resources being consumed
Solution Approach 1:
The system uses periodic background processing instead of continuous foreground processing. The fall detection algorithm executes periodically in the background, allowing the processor to enter low-power states between executions. This periodic action maintains fall detection capability while significantly reducing average power consumption compared to continuous processing.
Solution Approach 2:
The system introduces an intermediary layer between the sensor data and the fall detection algorithm. Data is first pre-processed and stored in a buffer, then the fall detection algorithm processes this buffered data periodically. This intermediary buffering mechanism allows the system to maintain real-time detection capability while enabling periodic rather than continuous processing, thus conserving battery life.
2Device complexity
If accelerometer data from mobile phones is used to detect fall events, then device complexity is reduced, but monitoring reliability deteriorates because mobile phones aren't always kept on the body
Solution Approach 1:
The wearable device performs fall detection autonomously using its own onboard accelerometer and processing capabilities. The device self-monitors its own motion data and executes the fall detection algorithm locally without requiring connection to or interaction with a mobile phone. This self-service approach ensures continuous monitoring availability while keeping the system relatively simple.
Solution Approach 2:
The system segments the fall detection function from the mobile phone and embeds it directly in the wearable device. The accelerometer data collection, fall detection algorithm, and alert generation are separated into the wearable device, while the mobile phone serves only as a supplementary communication channel. This segmentation ensures monitoring reliability by placing the critical detection function on the body at all times.
3Measurement precision
If high-rate communication is used between wearable device and server, then data accuracy is improved, but energy consumption deteriorates due to increased transmission frequency
Solution Approach 1:
The system dynamically changes the communication rate parameter based on detected events. During normal operation, communication occurs at a low rate or is suspended entirely. When a fall event is detected, the communication rate increases to transmit detailed activity logs and fall data to the server. This parameter change maintains data accuracy for critical events while minimizing energy consumption during routine periods.
Solution Approach 2:
The system applies different communication quality levels to different time periods and event types. Critical fall events receive high-rate, high-accuracy communication with detailed activity logs, while normal periods use minimal or no communication. This local quality approach ensures data accuracy is maintained where needed (during falls) while reducing overall energy consumption by lowering communication quality during non-critical periods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively discriminates fall severity while minimizing battery consumption and data transmission, enhancing monitoring efficiency and reducing unnecessary alerts, thus improving the effectiveness of fall detection and reducing medical costs.
Implementation Method 1
the fall monitor is configured to continuously record detected movement to an activity log
Data Source
AI summary
A system for detecting and discriminating a severity of a fall, the system, including a mobile device configured to communicate with a network; a notification module; and a smart watch configured to communicate with the mobile device, the smart watch including, so as to operate as background tasks, a fall monitor and an activity log resident on the smart watch, where the fall monitor is configured to record detected movement, from accelerometer data at a set sampling frequency, on the activity log. The notification module is configured to effect a setting of a predetermined rate of communication between the mobile device and the smart watch based on at least one of, at least part of a predetermined pattern of a fall discriminator within the activity log, and setting adjustment in the sampling frequency of the accelerometer data.