Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

Abstract

Due to the ever growing population of elderly people, there is a dramatic increase in fall accidents. Currently, multiple ideas exist to prevent the elderly from falling, by means of technology or individualised fall prevention training programs. Most of them are costly, difficult to implement or less used by the elderly, and they do not deliver the required results. Furthermore, the increasingly older population will also impact the workload of the medical and nursing personnel. Therefore, we propose a novel fall detection and warning system for nursing homes, relying on Bluetooth Low Energy wireless communication. This paper describes the hardware design of a fall-acceleration sensing wearable for the elderly. Moreover, the paper also focuses on a novel algorithm for real-time filtering of the measurement data as well as on a strategy to confirm the detected fall events, based on changes in the person’s orientation. In addition, we compare the performance of the algorithm to a machine learning procedure using a convolutional neural network. Finally, the proposed filtering technique is validated via measurements and simulation. The results show that the proposed algorithm as well as the convolutional neural network both results in an excellent accuracy when validating on a common database.

Wearable coupled-quarter-mode SIW antenna platform with hybrid kinetic and ambient-light energy harvesting

“…11 ). Recent applications of Bluetooth-Low-Energy (BLE) sensors show a current consumption of 9 mA during approximately 20 ms for the transmission of 1 advertisement packet 41 . The wireless ULP MCU applied here, being STM32WB35, features even better current consumption specifications of 5.2 mA in Tx mode at 0 dBm 42 .…”

Fall Detection and Warning System for Nursing Homes based on Bluetooth Low Energy

“…1. This wearable contains an accelerometer to monitor the movements of the elderly and was developed in an earlier publication [14]. It utilizes a rule based algorithm based on the retrieved accelerometer data.…”

“…To prevent the elderly from suffering serious injuries and trying to lower the stress level from the nursing personnel, a small unobtrusive fall detection and warning wearable was proposed in a previous publication [14]. Here, a proof-ofconcept for a low-cost fall detection and warning system based on Bluetooth Low Energy (BLE) [15] is proposed.…”

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

“…[9] discussed multiple approaches for FD in detail, i.e., wearable, ambient, multimodal, vision (images & videos), etc., on one platform of smart houses. [10] integrated a wireless approach targeted to predetermine FD with the aid of Bluetooth devices at hardware-level systems. In [11], time-domain, frequency-domain, and Hjorth-domain features are extracted using wearable approach.…”

“…[9] accumulated data with the triaxial Bosch BMA280. [10] employs UP-Fall Detection data. On the user side, [12] forgathered data from wearable devices.…”