Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

Diaz, Steven; Stephenson, John L.; Labrador, Miguel A.

doi:10.3390/app10010234

Cited by 88 publications

(82 citation statements)

References 119 publications

(316 reference statements)

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“…Nevertheless, in the past decades, walking speed has been useful to assess the health status (e.g., frailty and dementia) in clinical utilities. However, in recent year, there has been a high demand for new approaches that use low-cost-IMU-sensors-embedded gait assessment system to assess health status [ 48 ]. Specifically, clinical and research settings can use this system to precisely assess the health status with several gait variables (e.g., phases, variability, and gait asymmetry).…”

Section: Discussionmentioning

confidence: 99%

Associating Gait Phase and Physical Fitness with Global Cognitive Function in the Aged

Noh

Youm

Lee

et al. 2020

IJERPH

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This study aimed to identify classifier variables by considering both gait and physical fitness for identifying adults aged over 75 years and global cognitive function declines in older adults. The participants included 735 adults aged 65–89 years who were asked to walk at three different speeds (slower, preferred, and faster) while wearing inertial measurement units embedded in shoe-type data loggers and to perform nine physical fitness tests. The variability in the stance phase as well as the strength, balance, and functional endurance showed a strong dependence on the age being over 75 years. The cognitive function was evaluated by the Mini-Mental State Examination; a longer stance phase at a slower walking speed and decreased grip strength and five times sit-to-stand were associated with cognitive function. These findings may be useful for determining the decline in physical performance of older adults. A longer stance phase and decreased grip strength and five times sit-to-stand may be factors that help distinguish declines in cognitive function from normal age-related declines.

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Section: Discussionmentioning

confidence: 99%

Associating Gait Phase and Physical Fitness with Global Cognitive Function in the Aged

Noh

Youm

Lee

et al. 2020

IJERPH

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“…Wearable sensors that embed accelerometers and gyroscopes have been widely used to investigate posture and gait in subjects with PD [ 14 , 15 ]. However, the accuracy of the assessment using wearable sensors depends on sensor location, sensor-to-segment alignment, and, frequently, on the number of sensors, which increases the total cost and obtrusiveness of the setting [ 16 ]. The Wii Balance Board (WBB) (Nintendo, Kyoto, Japan) and the Kinect for Windows v2, also known as Kinect v2 (Microsoft, Redmond, WA, USA), in contrast, potentially enable human motion tracking without the limitations of wearable sensors.…”

Section: Introductionmentioning

confidence: 99%

Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson’s disease

Álvarez

Latorre

Aguilar

et al. 2020

J NeuroEngineering Rehabil

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Background Accurate assessment of balance and gait is necessary to monitor the clinical progress of Parkinson’s disease (PD). Conventional clinical scales can be biased and have limited accuracy. Novel interactive devices are potentially useful to detect subtle posture or gait-related impairments. Methods Posturographic and single and dual-task gait assessments were performed to 54 individuals with PD and 43 healthy controls with the Wii Balance Board and the Kinect v2 and the, respectively. Individuals with PD were also assessed with the Tinetti Performance Oriented Mobility Assessment, the Functional Gait Assessment and the 10-m Walking Test. The influence of demographic and clinical variables on the performance in the instrumented posturographic and gait tests, the sensitivity of these tests to the clinical condition and phenotypes, and their convergent validity with clinical scales were investigated. Results Individuals with PD in H&Y I and I.5 stages showed similar performance to controls. The greatest differences in posture and gait were found between subjects in H&Y II.5 and H&Y I–I.5 stage, as well as controls. Dual-tasking enhanced the differences among all groups in gait parameters. Akinetic/rigid phenotype showed worse postural control and gait than other phenotypes. High significant correlations were found between the limits of stability and most of gait parameters with the clinical scales. Conclusions Low-cost devices showed potential to objectively quantify posture and gait in established PD (H&Y ≥ II). Dual-tasking gait evaluation was more sensitive to detect differences among PD stages and compared to controls than free gait. Gait and posture were more impaired in akinetic/rigid PD.

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“…Wearable sensor technologies have been frequently applied to gait analyses. For example, Diaz et al [ 34 ] surveyed the applications of wearable sensor technologies in analyzing the gait, balance, and range of motion research. Nguyen et al [ 35 ] applied an IMU-based system to develop deep convolutional neural network models for distinguish subjects with foot structural abnormalities.…”

Section: Collection and Processingmentioning

confidence: 99%

Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units

Wang

Chen

Lin

et al. 2021

Sensors

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This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients.

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Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

Cited by 88 publications

References 119 publications

Associating Gait Phase and Physical Fitness with Global Cognitive Function in the Aged

Associating Gait Phase and Physical Fitness with Global Cognitive Function in the Aged

Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson’s disease

Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units

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