Metrics from in-home sensor data to assess gait change due to weighted vest therapy

Wallace, Robert B.; Abbott, Carmen; Gibson‐Horn, Cynthia; Rantz, Marilyn; Skubic, Marjorie

doi:10.1016/j.smhl.2017.03.002

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…41 The system includes numerous pattern recognition and machine learning algorithms. [42][43][44][45][46][47][48][49][50][51] Here, the focus will include the overall development and deployment using a human-centered approach, including the challenges faced as the system was adapted and tested in new settings. The project has included an interdisciplinary team of engineers and computer scientists along with the clinical team of nurses, social workers, physical therapists, and physicians, essential to the success of the system.…”

Section: Exemplar #3: Supporting Older Adults With Smart Sensor Technologymentioning

confidence: 99%

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges

et al. 2020

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Section: Exemplar #3: Supporting Older Adults With Smart Sensor Technologymentioning

confidence: 99%

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges

et al. 2020

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“…Human Activity Recognition(HAR) with wearable Internet of Things (IoT) sensors can be beneficial for the elderly and patients monitoring [7]. The increasing use of technology in healthcare causes better solutions for treatment, cost and time to a wide range of disease such as Parkinson [8], Multiple sclerosis(MS) [9], and elderly [10]. Parkinson's disease (PD) is a neurodegenerative disorder that affects neurons in the brain.…”

Section: Introductionmentioning

confidence: 99%

IoT-based Activity Recognition with Machine Learning from Smartwatch

Mozaffari¹,

Rezazadeh²,

Farahbakhsh³

et al. 2020

IJWMN

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Human activity recognition(HAR) with wearable Internet of Things (IoT) sensors can be beneficial for the elderly and patients monitoring. Smartwatches are the most accessible IoT devices that play an important role in human activity monitoring. The structure of an activity recognition system involves a platform that holds wearable sensors. Under the background, many platforms such as distributed sensors and smartphones and the combination of them have been investigated but platforms are still one of the main research challenges. Smartwatches can be more comfortable for the elderly and patients, therefore our research is focused on a smartwatch as an emerging IoT platform and machine learning method. The smartwatch attached to arm as the main position then was compared to other positions. We considered machine learning methods to present the smartwatch as a reliable platform in order to recognize activities, also we considered k-nearest neighbor and decision tree as two popular machine learning methods for activity recognition. We evaluated the performance with the confusion matrix and then we used accuracy and f1-score metrics for the result of our experiment. The metrics show accuracy and f1-score almost 99% as the performance of smartwatch on arm position.

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Gait Analysis for Normal and Crouch Gaits Applying No-Common Metrics in the Cartesian Space

González-Islas

Domínguez-Ramírez

López-Ortega

et al. 2021

Biosystems &Amp; Biorobotics

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Metrics from in-home sensor data to assess gait change due to weighted vest therapy

Cited by 4 publications

References 51 publications

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges

IoT-based Activity Recognition with Machine Learning from Smartwatch

Gait Analysis for Normal and Crouch Gaits Applying No-Common Metrics in the Cartesian Space

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