Posts Tagged Wearable sensors

[Abstract] mHealth technologies used to capture walking and arm use behavior in adult stroke survivors: a scoping review beyond measurement properties

Abstract

Purpose

We aimed to provide a critical review of measurement properties of mHealth technologies used for stroke survivors to measure the amount and intensity of functional skills, and to identify facilitators and barriers toward adoption in research and clinical practice.

Materials and methods

Using Arksey and O’Malley’s framework, two independent reviewers determined eligibility and performed data extraction. We conducted an online consultation survey exercise with 37 experts.

Results

Sixty-four out of 1380 studies were included. A majority reported on lower limb behavior (n = 32), primarily step count (n = 21). Seventeen studies reported on arm-hand behaviors. Twenty-two studies reported metrics of intensity, 10 reported on energy expenditure. Reliability and validity were the most frequently reported properties, both for commercial and non-commercial devices. Facilitators and barriers included: resource costs, technical aspects, perceived usability, and ecological legitimacy. Two additional categories emerged from the survey: safety and knowledge, attitude, and clinical skill.

Conclusions

This provides an initial foundation for a field experiencing rapid growth, new opportunities and the promise that mHealth technologies affords for envisioning a better future for stroke survivors. We synthesized findings into a set of recommendations for clinicians and clinician-scientists about how best to choose mHealth technologies for one’s individual objective.

  • Implications for Rehabilitation
  • Rehabilitation professionals are encouraged to consider the measurement properties of those technologies that are used to monitor functional locomotor and object-interaction skills in the stroke survivors they serve.
  • Multi-modal knowledge translation strategies (research synthesis, educational courses or videos, mentorship from experts, etc.) are available to rehabilitation professionals to improve knowledge, attitude, and skills pertaining to mHealth technologies.
  • Consider the selection of commercially available devices that are proven to be valid, reliable, accurate, and responsive to the targeted clinical population.
  • Consider usability and privacy, confidentiality and safety when choosing a specific device or smartphone application.

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[Abstract] Objective assessments of human motor ability of the upper limb: A systematic review

Abstract

BACKGROUND: Most of the patients who survive stroke, spinal cord or others nervous system injuries, must face different challenges for a complete recovery of physical functional impairment. An accurate and recurrent assessment of the patient rehabilitation progress is very important. So far, wearable sensors (e.g. accelerometers, gyroscopes) and depth cameras have been used in medical rehabilitation for the automation of traditional motor assessments. Combined with machine learning techniques, these sensors are leading to novel metric systems for upper limb mobility assessment.

OBJECTIVE: Review current research for objective and quantitative assessments of the upper limb movement, analyzing sensors used, health issues examined, and data processes applied such as: selected features, feature engineering approach, learning models and data processing techniques.

METHOD: A systematic review conducted according to the PRISMA guidelines. EBSCOHOST discovery service was queried for relevant articles published from January 2014 to December 2018 with English language and scholarly peer reviewed journals limits.

RESULTS: Of the 568 articles identified, 75 were assessed for eligibility and 43 were finally included and weighed for an in-depth analysis according to their ponderation. The reviewed studies show a wide use of sensors to capture raw data for subsequent motion analysis.

CONCLUSION: As the volume of the data captured via these sensors increase, it makes sense to extract useful information about them such as prediction of performance scores, detection of movement impairments and measured progression of recovery.

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[Abstract] Remote Monitoring of Physical Rehabilitation of Stroke Patients using IoT and Virtual Reality

Abstract

The statistics highlights that physical rehabilitation are required nowadays by increased number of people that are affected by motor impairments caused by accidents or aging. Among the most common causes of disability in adults are strokes or cerebral palsy. To reduce the costs preserving the quality of services new solutions based on current technologies in the area of physiotherapy are emerging. The remote monitoring of physical training sessions could facilitate for physicians and physical therapists’ information about training outcome that may be useful to personalize the exercises helping the patients to achieve better rehabilitation results in short period of time process. This research work aims to apply physical rehabilitation monitoring combining Virtual Reality serious games and Wearable Sensor Network to improve the patient engagement during physical rehabilitation and evaluate their evolution. Serious games based on different scenarios of Virtual Reality, allows a patient with motor difficulties to perform exercises in a highly interactive and non-intrusive way, using a set of wearable devices, contributing to their motivational process of rehabilitation. The system implementation, system validation and experimental results are included in the paper.

Source: https://ieeexplore.ieee.org/abstract/document/9183980

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[Abstract] Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors

Abstract

Objective: Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients’ response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis.

Methods: Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two.

Results: The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson’s correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimated with a Pearson’s correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson’s correlation of 0.94 between estimated and clinician-provided scores.

Conclusion: This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data.

Significance: This is important in the context of developing precision rehabilitation interventions.

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[Abstract] Gait Performance in People with Symptomatic, Chronic Mild Traumatic Brain Injury

Abstract

There is a dearth of knowledge about how symptom severity affects gait in the chronic (>3 months) mild traumatic brain injury (mTBI) population despite up to 53% of people reporting persisting symptoms after mTBI. The aim of this investigation was to determine whether gait is affected in a symptomatic, chronic mTBI group and to assess the relationship between gait performance and symptom severity on the Neurobehavioral Symptom Inventory (NSI). Gait was assessed under single- and dual-task conditions using five inertial sensors in 57 control subjects and 65 persons with chronic mTBI (1.0 year from mTBI). The single- and dual-task gait domains of Pace, Rhythm, Variability, and Turning were calculated from individual gait characteristics. Dual-task cost (DTC) was calculated for each domain. The mTBI group walked (domain z-score mean difference, single-task = 0.70; dual-task = 0.71) and turned (z-score mean difference, single-task = 0.69; dual-task = 0.70) slower (p < 0.001) under both gait conditions, with less rhythm under dual-task gait (z-score difference = 0.21; p = 0.001). DTC was not different between groups. Higher NSI somatic subscore was related to higher single- and dual-task gait variability as well as slower dual-task pace and turning (p < 0.01). Persons with chronic mTBI and persistent symptoms exhibited altered gait, particularly under dual-task, and worse gait performance related to greater symptom severity. Future gait research in chronic mTBI should assess the possible underlying physiological mechanisms for persistent symptoms and gait deficits.

Source: https://www.liebertpub.com/doi/abs/10.1089/neu.2020.6986

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[Abstract] An Omnidirectional Assistive Platform Integrated With Functional Electrical Stimulation for Gait Rehabilitation: A Case Study

Abstract

This paper presents a novel omnidirectional platform for gait rehabilitation of people with hemiparesis after stroke. The mobile platform, henceforth the “walker”, allows unobstructed pelvic motion during walking, helps the user maintain balance and prevents falls. The system aids mobility actively by combining three types of therapeutic intervention: forward propulsion of the pelvis, controlled body weight support, and functional electrical stimulation (FES) for compensation of deficits in angular motion of the joints. FES is controlled using gait data extracted from a set of inertial measurement units (IMUs) worn by the user. The resulting closed-loop FES system synchronizes stimulation with the gait cycle phases and automatically adapts to the variations in muscle activation caused by changes in residual muscle activity and spasticity. A pilot study was conducted to determine the potential outcomes of the different interventions. One chronic stroke survivor underwent five sessions of gait training, each one involving a total of 30 minutes using the walker and FES system. The patient initially exhibited severe anomalies in joint angle trajectories on both the paretic and the non-paretic side. With training, the patient showed progressive increase in cadence and self-selected gait speed, along with consistent decrease in double-support time. FES helped correct the paretic foot angle during swing phase, and likely was a factor in observed improvements in temporal gait symmetry. Although the experiments showed favorable changes in the paretic trajectories, they also highlighted the need for intervention on the non-paretic side.

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[ARTICLE] Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review – Full Text PDF

Digital biofeedback systems (DBSs) are used in physical rehabilitation to improve outcomes by engaging and educating patients and have the potential to support patients while doing targeted exercises during home rehabilitation. The components of feedback (mode, content, frequency and timing) can influence motor learning and engagement in various ways. The feedback design used in DBSs for targeted exercise home rehabilitation, as well as the evidence underpinning the feedback and how it is evaluated, is not clearly known. To explore these concepts, we conducted a scoping review where an electronic search of PUBMED, PEDro and ACM digital libraries was conducted from January 2000 to July 2019. The main inclusion criteria included DBSs for targeted exercises, in a home rehabilitation setting, which have been tested on a clinical population. Nineteen papers were reviewed, detailing thirteen different DBSs. Feedback was mainly visual, concurrent and descriptive, frequently providing knowledge of results. Three systems provided clear rationale for the use of feedback. Four studies conducted specific evaluations of the feedback, and seven studies evaluated feedback in a less detailed or indirect manner. Future studies should describe in detail the feedback design in DBSs and consider a robust evaluation of the feedback element of the intervention to determine its efficacy.

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via Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review – Sensors – X-MOL

 

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[Abstract] A novel motion detecting strategy for rehabilitation in smart home

Abstract

Recent years have witnessed the flourish in research and development efforts towards clinical rehabilitation systems in smart home applications. One of the most prior is that such systems are needed to provide real-time motion information about patients. In this paper, a motion detecting approach is proposed for efficiently understanding the human movement on the foundation of the Internet of Things (IoT) based architecture. Specifically, the detection algorithm is based on the fuzzy neural network (FNN), which learns to detect the variation among different gait phases. The moving phases as well as the instability of the tester are identified for recognition. On the tasks of identifying the subject motion using wearable sensing devices, this model achieves a significant high accuracy. Experimental results prove that the system is feasible for application designs and could be implemented on technological platforms.

via A novel motion detecting strategy for rehabilitation in smart home – ScienceDirect

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[ARTICLE] Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment – Full Text

Abstract

Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided.

Introduction

Stroke is one of the leading causes of disability worldwide [], with a global prevalence estimated at 42.4 million in 2015 []. Stroke results in permanent motor disabilities in 80% of cases []. During the acute and subacute stages (< 6 months after stroke []), patients receive rehabilitation therapies at specialized healthcare centers, consisting of an iterative process involving impairment assessments, goal definition, intervention, and progress evaluation []. After being discharged from the rehabilitation center (i.e. after entering the chronic stage, e.g., 6 months after stroke), 65% of patients are unable to integrate affected limbs into everyday-life activities [], showing a need for further treatment. Phrased differently, the rehabilitative process after stroke depends on the effective assessment of motor deficit and congruent allocation to treatment (diagnostics), accurate appraisal of treatment effects (recovery/adaptation evaluation), and prolonged treatment for continuous recovery during the chronic stage (extended training).

Each of these three aspects present practical challenges. Assigned treatments depend on the assessed early-stage disability []. A variety of assessment scales exist to evaluate motor impairment after stroke, designed to capture aspects such as joint range of motion (ROM), synergistic execution of movements, reaching and grasping capabilities, object manipulation, etc. []. These assessments are normally applied by specialized medical personnel, which entails certain variability between assessments []. Besides consistency in repeated measurements, some scales like the Fugl-Meyer assessment (FMA) [], are unable to capture the entire spectrum of motor function in patients due to limited sensitivity or ceiling effects [].

In addition to thorough standardized assessment scales, progress in patients is observable during the execution of activities of daily living (e.g., during occupational therapy sessions). Nevertheless, task completion not always reflects recovery, as patients often adopt different synergistic patterns to compensate for lost function [], and such behavior is not always evident.

Main provision of rehabilitation therapies occurs at hospitals and rehabilitation centers. Evidence of enhanced recovery related to more extensive training has been found [], but limited resources at these facilities often obstruct extended care during the chronic stage. This calls for new therapeutic options allowing patients to train intensively and extensively after leaving the treatment center, while ensuring the treatment’s quality, effectiveness and safety.

Wearable sensors used during regular assessments can reduce evaluation times and provide objective, quantifiable data on the patients’ capabilities, complementing the expert yet subjective judgement of healthcare specialists. These recordings are more objective and replicable than regular observations. They have the potential of reducing diagnostic errors affecting the choice for therapies and their eventual readjustment. Additional information (e.g., muscle activity) extracted during the execution of multiple tasks can be used to better characterize motor function in patients, allowing for finer stratification into more specific groups, which can then lead to better targeted care (i.e. personalized therapies). These devices also make it possible to acquire data unobtrusively and continuously, which enables the study of motor function while patients perform daily-life activities. Further, the prospect of remotely acquiring data shows promise in the implementation of independent rehabilitative training outside clinics, allowing patients to work more extensively towards recovery.

The objective of this review is to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity, aiming to present a roadmap for translating these technologies from “bench to bedside”. We selected articles based on their reports about tests conducted with actual stroke patients, with the exception of conductive elastomer sensors, on which extensive research exists without tests in patients. In the section “Wearable devices used in stroke patients”, we summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. In the “Discussion” section, we present suggestions concerning data acquisition and processing, as well as opportunities arising in this field, to guide future studies performed by clinicians and engineers alike.

Wearable devices used in stroke patients

Recent availability of ever more compact, robust and power-efficient wearable devices has presented research and development groups in academia and industry with the means of studying and monitoring activities performed by users on a daily basis.

Over the past years, multiple research groups have worked towards a reliable, objective and unobtrusive way of studying human movement. From the array of sensors and devices created, a few have gained popularity in time due to their practicality. The next subsections will focus on the wearable devices most frequently used in the study of human motion, with special emphasis on monitoring of upper limbs in stroke patients.

Inertial measurement units (IMUs)

Inertial measurement units (IMUs) are devices combining the acceleration readings from accelerometers and the angular turning rate detection of gyroscopes []. Recent versions of such devices are equipped with a magnetometer as well, adding an estimation of the orientation of the device with respect to the Earth’s magnetic field []. A general description of how inertial data are used to extract useful information from these devices is offered by Yang and Hsu []. High-end IMUs used for human motion tracking, such as the “MTw Awinda” sensor (Xsens®, Enscheda, Overijssel, The Netherlands) [], acquire data at sampling rates as high as 1 kHz (sensitivities of ±2000 deg/s, ±160 m/s2, ±1.9 G). More affordable sensors (e.g. “MMR” (mbientlab Inc.®, San Francisco, California, USA) []) stream data at 100 Hz (max sensitivities of ±2000 deg/s, ±16 g, 13 G). The necessary sampling rate depends on the application, and must be defined such that aliasing is avoided (i.e. Nyquist rate, 2 times the frequency of the studied phenomenon). Figure 1 shows an example of motion tracking using these devices.

Diagnostics

Multiple scales exist for assessing motor function in stroke patients []. However, limitations exist in terms of objectivity and test responsiveness to subtle changes [], as well as on the amount of time needed to apply these tests. Therefore, several research groups have focused on the use of IMUs to assess motor function more objectively. Hester et al. [] were able to predict hand and arm stages of the Chedoke-McMaster clinical score, while Yu et al. [] built Brunnstrom stage [] classifiers, assigning each patient to one of six classes of synergistic movements in affected limbs. The Wolf Motor test [], the FMA [] and the Action Research Arm Test (ARAT) [], frequently used to assess motor function in clinical settings, have also been automated.[…]

 

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[Abstract + References] Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination – Conference paper

Abstract

The work reintegration following shoulder biomechanical overload illness is a multidimensional process, especially for those tasks requiring strength, movement control and arm dexterity. Currently different robotic devices used for upper limb rehabilitation are available on the market, but these devices are not based on activities focused on the work reintegration. Furthermore, the rehabilitation programmes aimed to the work reintegration are insufficiently focused on the recovery of the necessary skills for the re-employment.

In this study the details of the design of an innovative robotic platform integrated with wearable sensors and virtual reality scenarios for upper limbs motor rehabilitation and visuomotor coordination is presented. The design of control strategy will also be introduced. The robotic platform is based on a robotic arm characterized by seven degrees of freedom and by an adaptive control, wearable sensorized insoles, virtual reality (VR) scenarios and the Leap Motion device to track the hand gestures during the rehabilitation training. Future works will address the application of deep learning techniques for the analysis of the acquired big amount of data in order to automatically adapt both the difficulty level of the VR serious games and amount of motor assistance provided by the robot.

References

  1. 1.
    MacEachen, E., et al.: Systematic review of the qualitative literature on return to work after injury. Scand. J. Work Environ. Health 32(4), 257–269 (2006)CrossRefGoogle Scholar
  2. 2.
    Franche, R.-L., Krause, N.: Readiness for return to work following injury or illness: conceptualizing the interpersonal impact of healthcare, workplace, and insurance factor. J. Occup. Rehabil. 12(4), 233–256 (2002)CrossRefGoogle Scholar
  3. 3.
    Hou, W.H., Chi, C.C., Lo, H.L.D., Kuo, K.N., Chuang, H.Y.: Vocational rehabilitation for enhancing return-to-work in workers with traumatic upper limb injuries (2013)Google Scholar
  4. 4.
    Shi, Q., Sinden, K., Macdermid, J.C., Walton, D., Grewal, R.: A systematic review of prognostic factors for return to work following work-related traumatic hand injury. J. Hand Ther. 27(1), 55–62 (2014)CrossRefGoogle Scholar
  5. 5.
    Fadyl, J., McPherson, K.: Return to work after injury: a review of evidence regarding expectations and injury perceptions, and their influence on outcome. J. Occup. Rehabil. 18(4), 362–374 (2008)CrossRefGoogle Scholar
  6. 6.
    Krebs, H.I.: Twenty + years of robotics for upper-extremity rehabilitation following a stroke. In: Rehabilitation Robotics (2018)CrossRefGoogle Scholar
  7. 7.
    Buongiorno, D., Sotgiu, E., Leonardis, D., Marcheschi, S., Solazzi, M., Frisoli, A.: WRES: a novel 3 DoF WRist exoskeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation. IEEE Robot. Autom. Lett. 3(3), 2152–2159 (2018)CrossRefGoogle Scholar
  8. 8.
    Krebs, H.I., et al.: Robotic applications in neuromotor rehabilitation. Robotica 21(1), 3–11 (2003)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lee, S.-S., Park, S.-A., Kwon, O.-Y., Song, J.-E., Son, K.-C.: Measuring range of motion and muscle activation of flower arrangement tasks and application for improving upper limb function. Korean J. Hortic. Sci. Technol. 30(4), 449–462 (2012)CrossRefGoogle Scholar
  10. 10.
    Spreeuwers, D., et al.: Work-related upper extremity disorders: one-year follow-up in an occupational diseases registry. Int. Arch. Occup. Environ. Health 84(7), 789–796 (2011)CrossRefGoogle Scholar
  11. 11.
    Mehrholz, J., Pohl, M., Platz, T., Kugler, J., Elsner, B.: Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke (2018)Google Scholar
  12. 12.
    Lederer, V., Rivard, M., Mechakra-Tahiri, S.D.: Gender differences in personal and work-related determinants of return-to-work following long-term disability: a 5-year cohort study. J. Occup. Rehabil. 22(4), 522–531 (2012)CrossRefGoogle Scholar
  13. 13.
    Siciliano, B., Lorenzo, S., Villani, L., Orilo, G.: Robotics: Modelling, Planning and Control, 2nd edn. Springer, London (2010).  https://doi.org/10.1007/978-1-84628-642-1CrossRefGoogle Scholar
  14. 14.
    Balasubramanian, S., Melendez-Calderon, A., Roby-Brami, A., Burdet, E.: On the analysis of movement smoothness. J. Neuroeng. Rehabil. 12, 112 (2015).  https://doi.org/10.1186/s12984-015-0090-9CrossRefGoogle Scholar
  15. 15.
    Berger, D.J., d’Avella, A.: Effective force control by muscle synergies. Front. Comput. Neurosci. 8, 46 (2014).  https://doi.org/10.3389/fncom.2014.00046CrossRefGoogle Scholar
  16. 16.
    Holzbaur, K.R.S., Murray, W.M., Delp, S.L.: A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann. Biomed. Eng. 33(6), 829–840 (2005).  https://doi.org/10.1007/s10439-005-3320-7CrossRefGoogle Scholar
  17. 17.
    Delp, S.L., et al.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007)CrossRefGoogle Scholar
  18. 18.
    Buongiorno, D., et al.: Evaluation of a pose-shared synergy-based isometric model for hand force estimation: towards myocontrol. In: Biosystems and Biorobotics (2017)Google Scholar
  19. 19.
    Buongiorno, D., Barsotti, M., Barone, F., Bevilacqua, V., Frisoli, A.: A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control: a case study on shoulder and elbow joints. Front. Neurorobot. 12, 74 (2018)CrossRefGoogle Scholar

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