BioMeT and Algorithm Challenges: A Proposed Digital Standardized Evaluation Framework

Godfrey, Alan; Goldsack, Jennifer C.; Tenaerts, Pamela; Coravos, Andrea; Aranda, Clara; Hussain, Azid; Barreto, Marcos; Young, Fraser; Vitório, Rodrigo

doi:10.1109/jtehm.2020.2996761

Cited by 18 publications

(11 citation statements)

References 15 publications

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“…Sensor use within healthcare research is becoming more prevalent, but it has often been reactive rather than proactive as innovation in this field can be quite fractious [ 32 , 33 ]. With continued uptake of emergent technologies, biomedical engineers must perform robust and vigorous bench testing (e.g.…”

Section: Low-cost Sensor Technologymentioning

confidence: 99%

Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design

Coulby

Clear

Jones³

et al. 2020

BioMed Eng OnLine

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Healthcare studies are moving toward individualised measurement. There is need to move beyond supervised assessments in the laboratory/clinic. Longitudinal free-living assessment can provide a wealth of information on patient pathology and habitual behaviour, but cost and complexity of equipment have typically been a barrier. Lack of supervised conditions within free-living assessment means there is need to augment these studies with environmental analysis to provide context to individual measurements. This paper reviews low-cost and accessible Internet of Things (IoT) technologies with the aim of informing biomedical engineers of possibilities, workflows and limitations they present. In doing so, we evidence their use within healthcare research through literature and experimentation. As hardware becomes more affordable and feature rich, the cost of data magnifies. This can be limiting for biomedical engineers exploring low-cost solutions as data costs can make IoT approaches unscalable. IoT technologies can be exploited by biomedical engineers, but more research is needed before these technologies can become commonplace for clinicians and healthcare practitioners. It is hoped that the insights provided by this paper will better equip biomedical engineers to lead and monitor multi-disciplinary research investigations.

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Section: Low-cost Sensor Technologymentioning

confidence: 99%

Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design

Coulby

Clear

Jones³

et al. 2020

BioMed Eng OnLine

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“…While the authors are not currently aware of any IMU-based technology to quantify step width during free-living, a computer vision approach has been suggested from a wearable camera [53]. Additionally, the outcome measures presented are primarily research-orientated, requiring a great deal of time-consuming post-processing and checking, which is based on prior experience of inertial data [56,57]. Therefore, there are needs to refine and deploy software that clinicians and patients can easily navigate, which would allow more widespread uptake and use by health professionals [57].…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Free-living gait does not differentiate chronic mTBI patients compared to healthy controls

Powell

Godfrey

Parrington

et al. 2022

J NeuroEngineering Rehabil

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Background Physical function remains a crucial component of mild traumatic brain injury (mTBI) assessment and recovery. Traditional approaches to assess mTBI lack sensitivity to detect subtle deficits post-injury, which can impact a patient’s quality of life, daily function and can lead to chronic issues. Inertial measurement units (IMU) provide an opportunity for objective assessment of physical function and can be used in any environment. A single waist worn IMU has the potential to provide broad/macro quantity characteristics to estimate gait mobility, as well as more high-resolution micro spatial or temporal gait characteristics (herein, we refer to these as measures of quality). Our recent work showed that quantity measures of mobility were less sensitive than measures of turning quality when comparing the free-living physical function of chronic mTBI patients and healthy controls. However, no studies have examined whether measures of gait quality in free-living conditions can differentiate chronic mTBI patients and healthy controls. This study aimed to determine whether measures of free-living gait quality can differentiate chronic mTBI patients from controls. Methods Thirty-two patients with chronic self-reported balance symptoms after mTBI (age: 40.88 ± 11.78 years, median days post-injury: 440.68 days) and 23 healthy controls (age: 48.56 ± 22.56 years) were assessed for ~ 7 days using a single IMU at the waist on a belt. Free-living gait quality metrics were evaluated for chronic mTBI patients and controls using multi-variate analysis. Receiver operating characteristics (ROC) and Area Under the Curve (AUC) analysis were used to determine outcome sensitivity to chronic mTBI. Results Free-living gait quality metrics were not different between chronic mTBI patients and controls (all p > 0.05) whilst controlling for age and sex. ROC and AUC analysis showed stride length (0.63) was the most sensitive measure for differentiating chronic mTBI patients from controls. Conclusions Our results show that gait quality metrics determined through a free-living assessment were not significantly different between chronic mTBI patients and controls. These results suggest that measures of free-living gait quality were not impaired in our chronic mTBI patients, and/or, that the metrics chosen were not sensitive enough to detect subtle impairments in our sample.

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“…Finally, the processing of IMU data requires specialized training and prior experience. 50,51 Therefore, a barrier to easily capturing and interpreting IMU data for clinical deployment is the development of "no-code" software that clinicians or non-technically skilled researchers can easily use. 51 To improve accessibility and transparency of data collection methodologies, future research should also focus on optimizing open-source approaches for physical activity and sleep detection, as proposed for waistworn sensors.…”

Section: [H2]limitationsmentioning

confidence: 99%

Exploring Inertial-Based Wearable Technologies for Objective Monitoring in Sports-Related Concussion: A Single-Participant Report

2022

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Objective Challenges remain in sports-related concussion (SRC) assessment to better inform return to play. Reliance on self-reported symptoms within the Sports Concussion Assessment Tool means that there are limited data on the effectiveness of novel methods to assess a player’s readiness to return to play. Digital methods such as wearable technologies may augment traditional SRC assessment and improve objectivity in making decisions regarding return to play. Methods The participant was a male university athlete who had a recent history of SRC. The single-participant design consisted of baseline laboratory testing immediately after SRC, free-living monitoring, and follow-up supervised testing after 2 months. The primary outcome measures were from traditional assessment (eg, Sports Concussion Assessment Tool and 2-minute instrumented walk/gait test; secondary outcome measures were from remote (free-living) assessment with a single wearable inertial measurement unit (eg, for gait and sleep). Results The university athlete (age = 20 years, height = 175 cm, weight = 77 kg [176.37 lb]) recovered and returned to play 20 days after SRC. Primary measures returned to baseline levels after 12 days. However, supervised (laboratory-based) wearable device assessment showed that gait impairments (increased step time) remained even after the athlete was cleared for return to play (2 months). Similarly, a 24-hour remote gait assessment showed changes in step time, step time variability, and step time asymmetry immediately after SRC and at return to play (1 month after SRC). Remote sleep analysis showed differences in sleep quality and disturbance (increased movement between immediately after SRC and once the athlete had returned to play [1 month after SRC]). Conclusion The concern about missed or delayed SRC diagnosis is growing, but methods to objectively monitor return to play after concussion are still lacking. This report showed that wearable device assessment offers additional objective data for use in monitoring players who have SRC. This work could better inform SRC assessment and return-to-play protocols. Impact Digital technologies such as wearable technologies can yield additional data that traditional self-report approaches cannot. Combining data from nondigital (traditional) and digital (wearable) methods may augment SRC assessment for improved return-to-play decisions.

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BioMeT and Algorithm Challenges: A Proposed Digital Standardized Evaluation Framework

Cited by 18 publications

References 15 publications

Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design

Towards remote healthcare monitoring using accessible IoT technology: state-of-the-art, insights and experimental design

Free-living gait does not differentiate chronic mTBI patients compared to healthy controls

Exploring Inertial-Based Wearable Technologies for Objective Monitoring in Sports-Related Concussion: A Single-Participant Report

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