Wearable Sensing for Solid Biomechanics

Wong, Charence; Zhang, Zhiqiang; Lo, Benny; Yang, Guang‐Zhong

doi:10.1109/jsen.2015.2393883

Cited by 78 publications

(56 citation statements)

References 112 publications

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“…Various quantifying systems that are used for selfsensing and self-monitoring combine wearable sensors, data acquisition techniques, and wearable computing. One such group of quantifying systems includes motion tracking and movement recognition applications [1][2][3][4][5]. Motion tracking can be performed at different scales-from lowprecision tracking in navigation and closed buildings [3] with required accuracies in metres to the submillimetre scale in the high-precision tracking of the fine movement of a speaker's lips and jaw [4].…”

Section: Introductionmentioning

confidence: 99%

Validation of smartphone gyroscopes for mobile biofeedback applications

Umek

Kos

2016

Pers Ubiquit Comput

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Smartphones are currently the most pervasive wearable devices. One particular use of smartphone inertial sensors is motion tracking in various mobile systems and applications. The objective of this study is to validate smartphone gyroscopes for angular tracking in mobile biofeedback applications. The validation method includes measurements of angular motion performed concurrently by a smartphone gyroscope and a professional optical tracking system serving as the reference. The comparison of the measurement results shows that the inaccuracies of a calibrated smartphone gyroscope for various movements are between 0.42°and 1.15°. Based on the measurement results and the general requirements of biofeedback applications, smartphone gyroscopes are sufficiently accurate for angular motion tracking in mobile biofeedback applications.

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Section: Introductionmentioning

confidence: 99%

Validation of smartphone gyroscopes for mobile biofeedback applications

Umek

Kos

2016

Pers Ubiquit Comput

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“…The typical equipment adopted in the laboratory includes marker-based optical and video processing systems, force sensing walkways and electromyography [3]. Besides its high performance and accuracy, this equipment is characterized by high costs, high complexity and technological constrains.…”

Section: Introductionmentioning

confidence: 99%

Multisensor System for Analyzing the Thigh Movement During Walking

et al. 2017

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Abstract-Body movements monitoring may allow preventing, diagnosing, and recovering several wrong attitudes that could lead to possible future diseases. A multi sensor system was developed to measure thigh movements in free-living environments all-day long especially during walking. The device is a very simple and portable system based on low-cost technology. It is composed of an inertial sensor and a strain sensor for detecting thigh movements, and of a microcontroller and a Bluetooth module. The measurement data are collected and sent wirelessly to a PC for storing and further analyses. We validated and tested the wearable system on 10 healthy subjects during walking and running by using an accurate inertial motion capture (Mo-cap) system. The inclination of the thigh measured by the proposed system has differed of maximum 5.5° with respect to the flexion/extension measured by the Mo-cap system. Furthermore, the experimental results have showed that the strain sensor output can also be related to the muscle activity during walking. This assumption has further been sustained when the system is tested during running. Thanks to its high wearability, wireless communication over long distances and long-battery life, the system could be adopted for domestic applications.Index Terms-strain sensor, inertial sensor, wearable sensor, wearable electronic device.

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“…Accurate joint angle estimation may be achieved through model constraint methods [15]–[17], fusion of inertial and magnetic sensing [18], [19], and multi-sensor fusion using cameras, GPS, and laser range finders (see [20] and references therein). However, model constraint methods are not resistant to erroneous measurements and additional sensors such as laser range finders and cameras add bulk to the system.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Dynamics-Based Estimator for Functional Electrical Stimulation: Preliminary Results From Lower-Leg Extension Experiments

Allen

Zhong

Kirsch

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Miniature inertial measurement units (IMUs) are wearable sensors that measure limb segment or joint angles during dynamic movements. However, IMUs are generally prone to drift, external magnetic interference, and measurement noise. This paper presents a new class of nonlinear state estimation technique called state-dependent coefficient (SDC) estimation to accurately predict joint angles from IMU measurements. The SDC estimation method uses limb dynamics, instead of limb kinematics, to estimate the limb state. Importantly, the nonlinear limb dynamic model is formulated into state dependent matrices that facilitate the estimator design without performing a Jacobian linearization. The estimation method is experimentally demonstrated to predict knee joint angle measurements during functional electrical stimulation of the quadriceps muscle. The nonlinear knee musculoskeletal model was identified through a series of experiments. The SDC estimator was then compared to an Extended Kalman filter (EKF), which uses a Jacobian linearization and a rotation matrix method, which uses a kinematic model instead of the dynamic model. Each estimator’s performance was evaluated against the true value of the joint angle, which was measured through a rotary encoder. The experimental results showed that the SDC estimator, the rotation matrix method, and EKF had root mean square errors of 2.70°, 2.86°, and 4.42°, respectively. Our preliminary experimental results show the new estimator’s advantage over the EKF method but a slight advantage over the rotation matrix method. However, the information from the dynamic model allows the SDC method to use only one IMU to measure the knee angle compared to the rotation matrix method that uses 2 IMUs to estimate the angle.

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Wearable Sensing for Solid Biomechanics

Cited by 78 publications

References 112 publications

Validation of smartphone gyroscopes for mobile biofeedback applications

Validation of smartphone gyroscopes for mobile biofeedback applications

Multisensor System for Analyzing the Thigh Movement During Walking

A Nonlinear Dynamics-Based Estimator for Functional Electrical Stimulation: Preliminary Results From Lower-Leg Extension Experiments

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