Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life

• Main Authors: Markus Lueken, Leo Mueller, Michel G. Decker, Cornelius Bollheimer, Steffen Leonhardt, Chuong Ngo
• Format: Article
• Language: English
• Published: MDPI AG 2020-12-01
• Series: Sensors
• Subjects: body sensor network; gait analysis; inertial sensors; ground reaction force
• Online Access: https://www.mdpi.com/1424-8220/20/24/7325

Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary <inline-formula><math display="inline"><semantics><mrow><mn>868</mn><mspace width="0.166667em"></mspace><mi>MHz</mi></mrow></semantics></math></inline-formula> communication interface. In particular, we present a sensor setup for gait analysis during everyday life monitoring. The arrangement consists of three identical inertial measurement sensors attached at the wrist, thigh, and chest. We additionally introduce a force-sensitive resistor integrated insole for measurement of ground reaction forces (GRFs), to enhance the assessment possibilities and generate ground truth data for inertial measurement sensors. Since the <inline-formula><math display="inline"><semantics><mrow><mn>868</mn><mspace width="0.166667em"></mspace><mi>MHz</mi></mrow></semantics></math></inline-formula> is not strongly represented in existing BSN implementations, we validate the proposed system concerning an application in gait analysis and use this as a representative demonstration of realizability. Hence, there are three key aspects of this project. The system is evaluated with respect to (I) accurate timing, (II) received signal quality, and (III) measurement capabilities of the insole pressure nodes. In addition to the demonstration of feasibility, we achieved promising results regarding the extractions of gait parameters (stride detection accuracy: <inline-formula><math display="inline"><semantics><mrow><mn>99.6</mn><mo>±</mo><mn>0.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, Root-Mean-Square Deviation (RMSE) of mean stride time: <inline-formula><math display="inline"><semantics><mrow><mn>5</mn><mspace width="0.166667em"></mspace><mi>ms</mi></mrow></semantics></math></inline-formula>, RMSE of percentage stance time: <inline-formula><math display="inline"><semantics><mrow><mn>2.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>). Conclusion: With the satisfactory technical performance in laboratory and application environment and the convincing accuracy of the gait parameter extraction, the presented system offers a solid basis for a gait monitoring system in everyday life.