Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings

Kelly, Damien P.; Esquivel, Karla Muñoz; Gillespie, James; Condell, Joan; Davies, Richard; Karim, Shvan; Nevala, Elina; Alamäki, Antti; Jalovaara, Juha; Barton, John; Tedesco, Salvatore; Nordström, Anna-Lena

doi:10.3390/s21134438

Cited by 8 publications

(4 citation statements)

References 44 publications

(131 reference statements)

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“…Most research on wearable sensor technologies currently places accuracy at the center of the design. This often comes at the expense of usability, which can ultimately have negative effects on continued device use [ 6 ]. Previous research suggests that, to achieve successful adoption of remote rehabilitation technologies, the solution must be both practical and usable [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

“…At present, most research on wearable sensor technology is developed with accuracy at the center of the study design. This often comes at the expense of usability [ 6 ] even though research studies have indicated that perceived ease of use is just as important as perceived usefulness when it comes to technology acceptance [ 7 ]. Indeed, Mancini and Horak [ 8 ] note that, to achieve successful adoption of remote rehabilitation technologies, the solution must be both practical and usable, which is of particular importance when considering wearable sensor systems.…”

Section: Introductionmentioning

confidence: 99%

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Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study

Esquivel¹,

Gillespie²,

Kelly³

et al. 2023

JMIR Aging

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Background The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. Objective This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. Methods Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. Results The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. Conclusions This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study

Esquivel¹,

Gillespie²,

Kelly³

et al. 2023

JMIR Aging

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“…Kelly et al [32] discussed the importance of accurate and objective clinical balanceassessment methods. A review was conducted on the state of the art related to technologyenabled balance-assessment methods and their potential to be utilised in remote rehabilitation settings.…”

Section: Applicationsmentioning

confidence: 99%

State-of-the-Art Sensors Research in Ireland

Barton

Ferguson

Mathúna

et al. 2022

Sensors

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“…Wearable sensors have the characteristics of convenience and a low price, providing researchers with a variety of possibilities and solutions [ 11 ]. Wearable sensors help rehabilitation patients to exercise at home, relieve travel pressure, and reduce psychological burden [ 12 , 13 ]. A variety of sensing devices are used to monitor patients’ vital signs and physiological responses, such as electromyography (EMG), electrocardiogram (ECG), and electroencephalogram (EEG), which can monitor the patient’s physical condition in real time.…”

Section: Introductionmentioning

confidence: 99%

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

Wei,

2023

Sensors

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The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

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Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings

Cited by 8 publications

References 44 publications

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study

State-of-the-Art Sensors Research in Ireland

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

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