Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models

Cresswell, Kellen Garrison; Shin, Yongyun; Chen, Shanshan

doi:10.3390/s17030466

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…This solution allows researchers to better identify activity or events. Relative to human walking, using these systems, it is possible to monitor events as the fall of a subject, or also to track in real time the trajectory of anatomical segments in total body configuration and to estimate the kinematic parameters of the gait cycle [8][9][10][11][12][15][16][17][18][19][20][21][22]. The system could be very useful for patients, caregivers, and even for clinicians to support their clinical decision, as long as it is: (a) a user-friendly system; (b) suitable to be used outdoors, in real life, and not only in the laboratory; (c) capable of monitoring the subject in natural conditions for a long period of time, without altering the natural and normal execution of movements and activities; and (d) without a very complicated calibration process.…”

Section: Introductionmentioning

confidence: 99%

“…No specific standard protocols were found in the literature, specifically dedicated to single trunk IMU systems for GA gold standard reference validation. Several studies have been carried out to assess the gait analysis reliability with wearable sensors [8][9][10][11][12][15][16][17][18][19][20][21][22], but the evaluation protocols have not been homogeneous; they are mostly based on recording gait trials in a controlled setup to produce some reference datasets. These datasets have then been used to validate the methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System

et al. 2018

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Miniaturized wearable Inertial Measurement Units (IMU) offer new opportunities for the functional assessment of motor functions for medicine, sport, and ergonomics. Sparse reliability validation studies have been conducted without a common specific approach and protocol. A set of guidelines to design validation protocol for these systems is proposed hereafter. They are based on the comparison between video analysis and the gold standard optoelectronic motion capture system for Gait Analysis (GA). A setup of the protocol has been applied to a wearable device implementing an inertial measurement unit and a dedicated harmonic oscillator kinematic model of the center of mass. In total, 10 healthy volunteers took part in the study, and four trials of walking at a self-selected speed and step length have been simultaneously recorded by the two systems, analyzed, and compared blindly (40 datasets). The model detects the steps and the foot which supports body weight. The stride time and the cadence have a mean absolute percentage error of 5.7% and 4.9%, respectively. The mean absolute percentage error in the measurement of step’s length and step’s speed is 5.6% and 13.5%, respectively. Results confirm that the proposed methodology is complete and effective. It is demonstrated that the developed wearable system allows for a reliable assessment of human gait spatio-temporal parameters. Therefore, the goal of this paper is threefold. The first goal is to present and define structured Protocol Design Guidelines, where the related setup is implemented for the validation of wearable IMU systems particularly dedicated to GA and gait monitoring. The second goal is to apply these Protocol Design Guidelines to a case study in order to verify their feasibility, user-friendliness, and efficacy. The third goal is the validation of our biomechanical kinematic model with the gold standard reference.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System

et al. 2018

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“…Unlike other gait parameters, which are calculated via sensor data from unilateral lower-limb movements, the temporal and spatial parameters of bilateral lower-limb movements are included in G s . A higher G s score indicates a more symmetrical gait [36,37], while a lower G s score indicates that the gait quality of the patient is still poor and more rehabilitation treatment is needed. Hemiplegia, caused by nervous system diseases such as strokes, is common in clinical practice, causing paralysis of one side of the body and asymmetrical gait.…”

Section: • Gait Symmetrymentioning

confidence: 99%

Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits

Qiu

Wang

et al. 2020

Sensors

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Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.

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“…Such flexibility can cause variations in data capture which impacts the gait assessment quality. This is one of the major challenges for self-administration of wearable sensors by users in an out-of-lab environment without any supervision [29]. In [29], a framework is proposed for quantifying the variations resulting from using wearable sensors for data capture in a free-living environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is one of the major challenges for self-administration of wearable sensors by users in an out-of-lab environment without any supervision [29]. In [29], a framework is proposed for quantifying the variations resulting from using wearable sensors for data capture in a free-living environment. Even though there are many factors that affect data capture by wearable sensors, they consider the four more important sources of variations including mounting location, mounting leg, sensors, and speed.…”

Section: Literature Reviewmentioning

confidence: 99%

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Soltaninejad

Cheng

Basu

2019

Sensors

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Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

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Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models

Cited by 8 publications

References 24 publications

Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System

Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System

Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

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