Body Orientation and Localization Monitoring

B.O.L.M. is a wearable wireless sensor network that enables spatial monitoring for people in need (disabled, elderly, patients)

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Elderly and people with special needs have to deal with the danger of falls which can lead to serious injuries and deterioration of their quality of life.

This project aims to develop a low cost, open architecture fall-detection and localization sensor network which can be deployed in different enviroments where patient monitoring is crucial (i.e. hospitals, elderly homes).

The network is composed of distributed wearable sensors and a central node connected to a PC via network. The mobile nodes are placed on the lumbar of the patient (using a harness) and are equipped with IMU and an RF tranceiver. The central node, equipped with a tranceiver, is responsible for collecting the data from the patient-nodes and transmitting them to a dedicated monitoring software on the local network. The sensor network is capable of providing a relative estimate, to the central-node, location info for every patient-node via RSSI and ToA measurements using the communication RF signals.

In more detail the BOLM sensor network has the following capabilities:

  • Calculate 3D orientation of the patient
  • Calculate the relative, to central node, location of the patient
  • Wireless communication to the central node with send-acknowledge and re-send in case of failure
  • Patient mobiliity analysis and classification into three states "Idle", "moving" and "fallen"
  • Visualization of the measurements, location and states of each patient in real-time
  • Open Source design using COTS components.
  • Can be extended to provide vitals information of the wearer and adjusted to provide monitoring in other situations were personel telemetry is needed in indoor enviornments.

Both the hardware and software of this project are maintened on GitHub:

  • Background

    Lefteris Kyriakakis08/02/2017 at 13:01 0 comments

    Falls are a clinical attribute of many diseases such as parkinson, heart failures, acoustic nerve defects and various movement dissabilities. More commonly, falls are a common occurance in elder people, according to the International Health Association approximately 28-35% of people above 65 years of age fall every year. Therefore, the research and development of movement classification, fall-detection and fall-prevention integrated systems, consists a necessicity for society to explore.

    This project aims to develop a wireless distributed monitoring solution that can be deployed in various enviroments and help in the immediate assistance of fall events both as a means of detection as well as a means of locating the person in need.

    The subject of fall-detection and movement classification was closely investigated by the co-author of this project as part of his BSc Thesis presented in: A Low Cost Open Architecture Fall Detection System Implemented as a WSN.

    Fig. 1 presents mixed movement patient data collected from two nodes in a WSN and successfully analyzed and classified in a custom MATLAB application:

    Figure 1. Mixed movements data and classification results from two nodes in real-time

    The subject of wireless node localization was investigated by the author of this project as part of his own BSc Thesis: Localization of a Mobile Node in a Wireless Sensor Network: an Evaluation of RSSI as a Distance Metric.

    Fig. 2 presents data collected from a mobile node, while static, 4 meters away from the central node.

    Figure 2. Static mobile node measurements at 4 meters from server node.

    In conclusion, this project aims to combine and advance the results of the authors 's background in order to develop a prototype deployable monitoring solution.

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