Using Body-Worn Sensors for Preliminary Rehabilitation Assessment in Stroke Victims With Gait Impairment

Qiu, Sen; Wang, Zhelong; Zhao, Hongyu; Liu, Long; Jiang, Yongmei

doi:10.1109/access.2018.2816816

Cited by 53 publications

(43 citation statements)

References 39 publications

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“…Qiu et al proposed a method that used body-worn sensors to collect the gait data for the assessment of stroke patients. ey have found that the gait analysis has a huge contribution towards the diagnosis and treatment of the stroke patients and mentioned that a wearable sensor-based gait analysis system has the potential for supporting rehabilitation in the clinics and hospitals [17]. Byun et al have proposed a method that uses the wearable accelerometer to measure the gait characteristics of older people having normal cognition.…”

Section: Related Workmentioning

confidence: 99%

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients

Aich

Pradhan

Chakraborty

et al. 2020

Journal of Healthcare Engineering

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In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson’s disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.

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Section: Related Workmentioning

confidence: 99%

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients

Aich

Pradhan

Chakraborty

et al. 2020

Journal of Healthcare Engineering

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show abstract

“…Moreover, in practice, the measurement error of MEMS magnetometer is non-negligible, the magnetometer measurement error mainly includes environmental interference and inherent error [ 12 , 37 ]. Researchers have conducted various calibration methods using high precision instruments and equipments [ 38 , 43 , 44 ], however, these methods are time-consuming and the calibration effect largely relies on the precision of equipment.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, specific space is normally needed to perform gait analysis using the above systems. In particular, the camera system may need more than 100 hundred square meters [ 10 , 11 , 12 , 13 ]. Table 2 lists a brief comparison of mainstream gait analysis methods.…”

Section: Related Workmentioning

confidence: 99%

MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

et al. 2018

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Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment.

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“…In this case, the gravity vector should be derived correctly. During the gait cycle, assuming that the accelerometer offset is invariable, it is feasible to set the foot speed to zero at the end of each swing phase, depending on the periodicity of the person's walking [38].…”

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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Using Body-Worn Sensors for Preliminary Rehabilitation Assessment in Stroke Victims With Gait Impairment

Cited by 53 publications

References 39 publications

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients

MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

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

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