This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements.

This paper presents a method for real-time shape sensing of thin and flexible materials, such as fabric. A method is based on three-axial acceleration and magnetic sensor nodes that are embedded into the material and can measure local orientation data. A proposed algorithm for global shape reconstruction from local orientation measurements ensures fast computations for shape reconstruction utilizing data from large number of sensors. This provides basis for the development of portable self-contained real-time systems for free form shape sensing with high resolution that has not been demonstrated before. A method is designed for applications in new emerging fields, such as smart textile and flexible electronics, where it can be used to obtain wearers posture or shape of the device. A method can be implemented with off the shelf low-cost electronic components and conventional portable computing devices, such as smartphone. The practical implementation of the system is demonstrated, also assessment of shape reconstruction performance is done by comparison with commercial Kinect v2 sensor. Impact of different error sources is analyzed, and possible improvements of the method are discussed.

In this paper a method for efficient data acquisition from sensor network with large number of nodes is proposed. A specific design allows simple device connection in daisy-chain configuration, thus removing addressing problems and allowing synchronized sampling design using just 4 wires. Several limitations are considered to provide mathematical model for estimation of maximum number of sensor nodes. An experimental setup was designed which demonstrated theoretical ability to acquire data from up to 200 sensors with 50 Hz sampling rate using conventional low-cost hardware, however, more advanced setup could allow to increase maximum number of sensors several times. The wiring structure of the network as well as hardware is specifically designed for use in smart garments or similar applications that require sensor integration in compact environment and low-power, real-time performance.

Journal of Medical and Bioengineering (JOMB)

The paper proposes flexible system that is based on Robot Operating System framework for integration of 3D computer vision and artificial intelligence algorithms with industrial robots for...