A Bluetooth-low-energy-based detection and warning system for vulnerable road users in the blind spot of vehicles

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

Development of low-cost and low-power wearable sensor nodes based on bluetooth low energy

Dit proefschrift beschrijft de ontwikkeling van draadloze sensor nodes (WSN) die Bluetooth Low Energy als communicatieprotocol gebruiken en wordt toegepast op drie verschillende topics. Het eerste onderwerp gaat over dodehoekdetectie bij vrachtwagens. Er worden enkele nodes op een vrachtwagen gemonteerd en de zwakke weggebruiker draagt een zelfontworpen wearable. Deze maakt gebruik van een energiezuinig regel-gebaseerd algoritme dat bepaalt of een zwakke weggebruiker te dicht in de buurt komt van de vrachtwagen. Het volledige systeem wordt getest in een real-life situatie. Later wordt een van deze nodes geminiaturiseerd. De tweede topic handelt over valdetectie bij ouderen in woonzorgcentra. Een kleine onopvallende wearable wordt ontwikkeld dat op basis van accelerometer data zal bepalen of er zich een val heeft voorgedaan. Op basis van drie vrij beschikbare databanken is het zelfontworpen regel-gebaseerde algoritme getest en vergeleken met een CNN algoritme. In een volgende stap werden de resterende nodes van het systeem ontwikkeld en getest in laboratoriumomstandigheden. Monitoren van trein integriteit is de laatste topic in dit proefschrift. Er worden verschillende metingen uitgevoerd op en rond een locomotief met verschillende wagons. Op basis van deze metingen wordt het uitgerolde WSN energie-efficiënt gemaakt en voorzien van een antenne voor stabiele communicatie

BLE-based power efficient WSN for industrial IoT train integrity monitoring

Monitoring train integrity is a very important topic for economical, management and safety reasons. Knowing the localization, volume and other parameters is very valuable for most train and large industry companies. Current literature focuses on placing many sensors in a Wireless Sensor Network (WSN) around the train wagons, but do not take battery lifetime into perspective. With the increasing interest in industrial Internet of Things (IoT) applications, the connectivity and battery lifetime are very important parameters. In this paper, the vibrations measured on train wagons are analyzed in order to find the most optimal trigger point to wake up or to put the WSN in a deep sleep mode. This way, a large amount of power can be saved and extend the battery lifetime significantly. Furthermore, several Received Signal Strength Indicator (RSSI) measurements were performed to find the optimal Tx level and antenna topology for communication between different wireless sensor nodes on the train wagon. The proposed measurements show that an inexpensive accelerometer and a prefabricated antenna are perfectly usable in the WSN. RF measurements on the brakes results in an average Package Receive Rate (PRR) of approximately 92 % and an Average Received Power (ARP) of around −83 dBm starting from a Tx level of 4 dBm

Fall detection and warning system for nursing homes based on bluetooth low energy

Fall accidents are a frequent problem with the elderly and lead to severe injuries and/or could have a lethal ending. To prevent these potential deaths the nursing personnel visits the elderly on a regular basis. This has an enormous influence on the mental and physical capabilities of the nursing personnel. In combination with the ever-growing presence of Internet of Things (IoT) applications, this paper proposes a low-power wireless fall detection and warning system based on Bluetooth Low Energy (BLE). The aim of the system is to lower the workload of the nursing personnel and prevent elderly from dying from hypothermia. The system consists of a patient wearable (P) monitoring the movement of the elderly, a Detection Node (DN) scanning the room of the elderly to pinpoint the position of the fallen elderly, multiple Network Nodes (NNs) in the hallways sending the alert messages to the closest caretaker wearing a Caretaker Node (CN). This node visualizes all vital parameters, so the nursing personnel can help in the fastest way possible. A proof-of-concept is proposed in this paper, together with measurements and power analysis

Wearable bluetooth low energy based miniaturized detection node for blind spot detection and warning system on vehicles

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

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

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