Multi Sensor System for Analyzing the Thigh Movement during Walking (original) (raw)
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Benini L: A Wireless System for Gait and Posture Analysis Based
2015
Abstract- In this paper we describe a wireless wearable system to monitor gait, based on a customized pair of commercial insoles able to collect ground reaction forces by use of 24 embedded cells for each foot. Each insole was combined with a small form factor, low-power Inertial Measurement Unit (IMU) and enabled to communicate via Bluetooth with a base station. We present here the characterization of the system both in terms of performance and in terms of functionality. The system was tested on a subject to demonstrate the usability and the features extraction during gait; this data allow to recognize walking phase in terms of swing and stance phase, step and stride duration, double support and single support duration, both using the pressure sensors and the IMU.
Procedia Engineering, 2016
Biomechanical analysis of movement during sport practice is extremely useful to assess and, subsequently, optimise movement performance during sport which can also assist athletes during rehabilitation following injury (such as Anterior Cruciate Ligament reconstruction). It is mostly performed using camera-based motion analysis systems, which provide good results but present serious drawbacks (for instance, consistent size, high cost, and lack of portability). Thus, small-size low-cost wearable sensors are an emerging tool for biomechanics monitoring. Aim of the present work is to implement a novel wireless portable easy-to-use system, consisting of two Tyndall Wireless Inertial Measurement Units (WIMUs) per leg, suitable for free-living environments and able to provide a complete biomechanics assessment (generated on a report) without the constraints of a laboratory. Validation for the lower-limbs using state-of-the-art camera-based motion capture is presented here. Algorithms are implemented in Matlab, and the scenarios considered simulate a free-living environment and exercises performed in a rehabilitation procedure. The system has been validated with healthy and impaired subjects. This novel system shows high accuracy values for all considered scenarios. Moreover, it is able to detect atypical movement characteristics. The results of this feasibility study support the next phase which will be to assess the external and ecological validity of athletes' on-field movement performance, which will help to inform individualised training protocols or enhance targeted rehabilitation programmes.
Sensors, 2020
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 868MHz 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 868MHz is not strongly represented in ...
A wearable multi-sensor system for real world gait analysis
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
Gait analysis is commonly performed in standardized environments, but there is a growing interest in assessing gait also in ecological conditions. In this regard, an important limitation is the lack of an accurate mobile gold standard for validating any wearable system, such as continuous monitoring devices mounted on the trunk or wrist. This study therefore deals with the development and validation of a new wearable multi-sensor-based system for digital gait assessment in free-living conditions. In particular, results obtained from five healthy subjects during lab-based and realworld experiments were presented and discussed. The in-lab validation, which assessed the accuracy and reliability of the proposed system, shows median percentage errors smaller than 2% in the estimation of spatio-temporal parameters. The system also proved to be easy to use, comfortable to wear and robust during the out-of-lab acquisitions, showing its feasibility for free-living applications. I. INTRODUCTION Recent literature has shown the relevance of characterising an individual's mobility in real-world conditions for a complete assessment of typical motor abilities [1,2]. This requires the use of activity monitors, e.g. devices including a single inertial measurement unit (IMU), that can be used without causing discomfort thanks to its limited invasivity. In this sense, the most convenient body positionings are trunk and wrist [3]. However, those locations present criticalities for the analysis of gait in terms of reliability, since the farther from the contact point the IMU is placed, the more difficult the estimation of gait-related parameters is. In this respect, the trunk is far from the ground but near to the centre of mass while the wrist is far from both ground and centre of mass. Although the scientific community is actively working on developing and improving algorithms for the above-mentioned solutions, algorithms validation is still performed in the laboratory while capturing simple gait tasks in spatially and temporally limited observation windows [4,5]. Testing single-sensor algorithms outside the laboratory would require a wearable system that is robust and accurate enough to be used as reference in validating other wearable technologies, i.e. a mobile gold standard
Smart Insole: A Wearable Sensor Device for Unobtrusive Gait Monitoring in Daily Life
IEEE Transactions on Industrial Informatics, 2016
Gait analysis is an important medical diagnostic process and has many applications in healthcare, rehabilitation, therapy, and exercise training. However, typical gait analysis has to be performed in a gait laboratory, which is inaccessible for a large population and cannot provide natural gait measures. In this paper, we present a novel sensor device, namely, Smart Insole, to tackle the challenge of efficient gait monitoring in real life. An array of electronic textile (eTextile) based pressure sensors are integrated in the insole to fully measure the plantar pressure. Smart Insole is also equipped with a low-cost inertial measurement unit including a 3-axis accelerometer, a 3-axis gyroscope, and a 3axis magnetometer to capture the gait characteristics in motion. Smart Insole can offer precise acquisition of gait information. Meanwhile, it is lightweight, thin, and comfortable to wear, providing an unobtrusive way to perform the gait monitoring. Furthermore, a smartphone graphic user interface is developed to display the sensor data in real-time via Bluetooth low energy. We perform a set of experiments in four real-life scenes including hallway walking, ascending/descending stairs, and slope walking, where gait parameters and features are extracted. Finally, the limitation and improvement, wearability and usability, further work, and healthcare-related potential applications are discussed.
Designing a new wearable and Wireless Inertial Measurement Unit for the physical activity monitoring
2018
Physical activity monitoring is important to record all the daily physical activities for the purpose of fitness and health. This motive caused a tremendous progress in the wearable technologies. Current project encompasses 9 degree of freedom inertial sensor (triaxial accelerometer, gyroscope, and magnetometer) with Wi-Fi communication in a very small wearable data logger integrated with a web server. It is applicable as on-body sensor network for more intricate activity recognition applications. Inertial sensor data measured and transferred to either a custom designed web server in online mode or stored in a MicroSD memory card in the offline mode. Wearable data logger with 18×30×30 mm (W×L×H) and 20 grams weight designed and produced. The system was tested at 200 Hz during online mode and acceptable precision and noise ascertained. Current device provided movement recording with wireless communication in small size and low cost to be applicable in the health and fitness applicati...
Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking
Sensors
A person’s walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the cloth...
Wearable Sensor System for Monitoring Body Kinematics
— Existing human body motion capture solutions rely on camera based systems limited to confined measurements, or Inertial Measurement Units (IMUs) prone to noise and drift, resulting in position inaccuracies. This investigation demonstrates a proof-of-concept wearable sensor system which accurately monitors human body kinematics in real-time using Radio Frequency (RF) positioning sensors combined with MEMS based IMU sensors. In certain IMU orientations, we measured an average pitch error of < 2 degrees for the combined method, compared with 12 degrees for an IMU alone. This self-contained sensor network has applications including military training, gaming, sports and healthcare.
Journal of Sensor and Actuator Networks
A SmartInsoles Cyber-Physical System (CPS) is designed and implemented for the purpose of measuring gait parameters of multiple users in a restriction-free environment. This CPS comprises a master software installed on a computer and numerous multi-sensory health devices in the form of smart insoles. Each of these insoles contains 12 Force-Sensitive Resistor (FSR) sensors, an Inertial Measurement Unit (IMU), a WiFi-enabled microcontroller and a battery to power all components. A validation pilot study was completed in collaboration with the Interdisciplinary School of Health Sciences at the University of Ottawa by performing 150 trials on 15 healthy subjects. Each subject performed 10 walks on the Tekscan Strideway gait mat system, while simultaneously wearing the designed SmartInsoles CPS. Spatiotemporal data for over 450 unique steps were collected by both systems. These data were analyzed carefully, and a thorough comparison was performed between the results from the two systems....