MotorBrain: A mobile app for the assessment of users’ motor performance in neurology

Vianello, Andrea; Chittaro, Luca; Burigat, Stefano; Budai, Riccardo

doi:10.1016/j.cmpb.2017.02.012

Cited by 21 publications

(14 citation statements)

References 31 publications

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“…Previous validations of smartphone hardware to evaluate movement [7–9] demonstrate smartphone technology is acceptable for recording human movement and allows us to focus on validating that our ADL modeling results in accurate counting of repetitions of mRehab ADLs and reliable measurements across days.…”

Section: Introductionmentioning

confidence: 96%

“…Furthermore, motion data collected by the inertial sensors in both Apple and Android smartphones, has been validated using a clinical motion capture system [8]. Touch screen technology can also capture timing of human movement for tasks such as tapping [9]. The smartphone represents portable technology capable of accurately quantifying movement and providing user feedback on motor performance through visual display and/or audio systems.…”

Section: Introductionmentioning

confidence: 99%

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Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement

Bhattacharjya

Stafford

Cavuoto

et al. 2019

J NeuroEngineering Rehabil

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BackgroundResidual sensorimotor deficits are common following stroke. While it has been demonstrated that targeted practice can result in improvements in functional mobility years post stroke, there is little to support rehabilitation across the lifespan. The use of technology in home rehabilitation provides an avenue to better support self-management of recovery across the lifespan. We developed a novel mobile technology, capable of quantifying quality of movement with the purpose of providing feedback to augment rehabilitation and improve functional mobility. This mobile rehabilitation system, mRehab, consists of a smartphone embedded in three dimensional printed items representing functional objects found in the home. mRehab allows individuals with motor deficits to practice activities of daily living (ADLs) and receive feedback on their performance. The aim of this study was to assess the usability and consistency of measurement of the mRehab system.MethodsTo assess usability of the mRehab system, four older adults and four individuals with stroke were recruited to use the system, and complete surveys to discuss their opinions on the user interface of the smartphone app and the design of the 3D printed items. To assess the consistency of measurement by the mRehab system, 12 young adults were recruited and performed mRehab ADLs in three lab sessions within 1 week. Young adults were chosen for their expected high level of consistency in motor performance.ResultsUsability ratings from older adults and individuals with stroke led us to modify the design of the 3D printed items and improve the clarity of the mRehab app. The modified mRehab system was assessed for consistency of measurement and six ADLs resulted in coefficient of variation (CV) below 10%. This is a commonly used CV goal for consistency. Two ADLs ranged between 10 and 15% CV. Only two ADLs demonstrated high CV.ConclusionsmRehab is a client-centered technology designed for home rehabilitation that consistently measures performance. Development of the mRehab system provides a support for individuals working on recovering functional upper limb mobility that they can use across their lifespan.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement

Bhattacharjya

Stafford

Cavuoto

et al. 2019

J NeuroEngineering Rehabil

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“…As individuals age, challenges can arise with using digital health technology primarily due to the progressive decline of many physiological (e.g., sight or hearing) and neurocognitive functions (e.g., memory, executive function, and motor issues), in addition to an increased susceptibility to certain neurodegenerative diseases (e.g., Parkinson's disease (PD), Alzheimer's disease, and ALS) [40,53,54,57]. Traditionally, neurocognitive functions of interest have been assessed in clinical settings using various accepted pen-and-paper style assessments (e.g., Montreal Cognitive Assessment (MoCA) [31], Mini Mental State Examination (MMSE) [49], and the Menu Task Assessment (MT) [1]) [52]. However, these assessments only evaluate a subset of relevant neurocognitive functions [47].…”

Section: Introductionmentioning

confidence: 99%

Design of a neurocognitive digital health system (NDHS) for neurodegenerative diseases

Templeton

Poellabauer

Schneider

2021

Proceedings of the 2021 Workshop on Future of Digital Biomarkers

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Digital health technology is becoming more ubiquitous in monitoring individuals' health as both device functionality and overall prevalence increase. However, as individuals age, challenges arise with using this technology particularly when it involves neurodegenerative issues (e.g., for individuals with Parkinson's disease, Alzheimer's disease, and ALS). Traditionally, neurodegenerative diseases have been assessed in clinical settings using pen-and-paper style assessments; however, digital health systems allow for the collection of far more data than we ever could achieve using traditional methods. The objective of this work is the formation and implementation of a neurocognitive digital health system designed to go beyond what pen-and-paper based solutions can do through the collection of (a) objective, (b) longitudinal, and (c) symptom-specific data, for use in (d) personalized intervention protocols. This system supports the monitoring of all neurocognitive functions (e.g., motor, memory, speech, executive function, sensory, language, behavioral and psychological function, sleep, and autonomic function), while also providing methodologies for personalized intervention protocols. The use of specifically designed tablet-based assessments and wearable devices allows for the collection of objective digital biomarkers that aid in accurate diagnosis and longitudinal monitoring, while patient reported outcomes (e.g., by the diagnosed individual and caregivers) give additional insights for use in the formation of personalized interventions. As many interventions are a one-size-fits-all concept, digital health systems should be used to provide a far more comprehensive understanding of neurodegenerative conditions, to objectively evaluate patients, and form personalized intervention protocols to create a higher quality of life for individuals diagnosed with neurodegenerative diseases. CCS CONCEPTS• Applied computing → Health care information systems; • Information systems → Information systems applications; • Human-centered computing → Interaction devices.

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“…The technological advancements have allowed health care programs to be effective, accessible and sustainable. Mobile-based cognitive programs in the form of "Applications (Apps)" are now widely utilized for screening and assessment of persons with disability [1] [2]. Some studies have shown the utility of mobile-based administration can induce higher interest in patients with mental disorder [3] [4].…”

Section: Iintroductionmentioning

confidence: 99%

Effect of Computerized Cognitive Remediation in Patients with Schizophrenia

Singh,

Singh

2020

IJRTE

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The objective of this study is to evaluate the effect of computerized cognitive remediation through 3 months randomized controlled trial in Indian patients with schizophrenia. Nineteen chronic schizophrenic patients were recruited and randomized into Cognitive Training (CT) and Treatment as Usual (TAU) groups. Neuropsychological assessments were done before and after 3 months of cognitive training on the developed program. As compared to TAU, the CT group exhibited significant improvement in speed of processing (p= 0.031, 95% Confidence Interval CI [-90.36 to -29.64], large effect size Φ = 0.7) and sustained attention (digit span -time, (p= 0.015, 95% CI [-99.40 to -41.60], large effect size Φ = 0.7)). The outcome of this study shows that the computerized cognitive training is feasible and useful in treating cognitive deficits in Indian patients with schizophrenia.

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MotorBrain: A mobile app for the assessment of users’ motor performance in neurology

Cited by 21 publications

References 31 publications

Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement

Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement

Design of a neurocognitive digital health system (NDHS) for neurodegenerative diseases

Effect of Computerized Cognitive Remediation in Patients with Schizophrenia

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