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.

Blind spot road accidents are a frequently occurring problem. Every year, several deaths are caused by this phenomenon, even though a lot of money is invested in raising awareness and in the development of prevention systems. In this paper, a blind spot detection and warning system is proposed, relying on Received Signal Strength Indicator (RSSI) measurements and Bluetooth Low Energy (BLE) wireless communication. The received RSSI samples are threshold-filtered, after which a weighted average is computed with a sliding window filter. The technique is validated by simulations and measurements. Finally, the strength of the proposed system is demonstrated with real-life measurements.

Annually, approximately 10 people are involved in a lethal blind spot accident on Belgian roads, even though a lot of money is invested in the development of blind spot detection systems and in raising the awareness of this phenomenon. In previous research, we developed a blind spot detection and warning system based on Bluetooth Low Energy (BLE) and received signal strength indicator (RSSI) measurements. In this paper, the miniaturization of the detection node and wearable is presented. There will be a closer look at the development of the Printed Circuit Board (PCB) and the folded Shorted Patch (S-P) antenna that will be integrated into the side lights of trailers. In a future step, the wearable design will be updated with the same miniaturization steps taken in this paper.