Beyond the standard clinical rating scales: Fine-grained assessment of post-stroke motor functionality using wearable inertial sensors

Zhang, Mi; Lange, Belinda; Chang, Ching-Lung; Sawchuk, Alexander A.; Rizzo, Albert

doi:10.1109/embc.2012.6347388

Cited by 33 publications

(49 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, IMUs do not interfere with the wearer’s movement. Several studies have used IMUs for analyzing gait and other movements as an inexpensive and unobtrusive substitute for other technologies [3]–[5]. In addition, we have shown in previous work that longitudinal changes in IMU metrics are predictive of patient FIM scores at discharge from inpatient rehabilitation [6].…”

Section: Introductionmentioning

confidence: 81%

“…The TUG test measures the time required to rise from a seated position in a chair, walk out 3 m, walk back to the chair, and sit down. Assessments like the TUG provide a high level overview of patient mobility, but are not sensitive enough to capture individual limb movements or changes in mobility and gait features [3]. More precise quantitative measurements of patient performance during rehabilitation can be collected via pervasive technology, such as wearable inertial measurement units (IMUs).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring changes in gait and vehicle transfer ability during inpatient rehabilitation with wearable inertial sensors

Борисов

Sprint

Cook

et al. 2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

View full text Add to dashboard Cite

Restoration of functional independence in gait and vehicle transfer ability is a common goal of inpatient rehabilitation. Currently, ambulation changes tend to be subjectively assessed. To investigate more precise objective assessment of progress in inpatient rehabilitation, we quantitatively assessed gait and transfer performances over the course of rehabilitation with wearable inertial sensors for 20 patients receiving inpatient rehabilitation services. Secondarily, we asked physical therapists to provide feedback about the clinical utility of metrics derived from the sensors. Participant performance was recorded on a sequence of ambulatory tasks that closely resemble everyday activities. We developed a custom software system to process sensor signals and compute metrics that characterize ambulation performance. We quantify changes in gait and transfer ability by performing a repeated measures comparison of the metrics one week apart. Metrics showing the greatest improvement are walking speed, stride regularity, acceleration root mean square, walking smoothness, shank peak angular velocity, and shank range of motion. Furthermore, feedback from physical therapists suggests that wearable sensor-derived metrics can potentially provide rehabilitation therapists with additional valuable information to aid in treatment decisions.

show abstract

Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Measuring changes in gait and vehicle transfer ability during inpatient rehabilitation with wearable inertial sensors

Борисов

Sprint

Cook

et al. 2017

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

View full text Add to dashboard Cite

show abstract

“…Various studies in controlled settings show that IMUs can track arm, hand, and finger movements [11-14]. This line of research is typically focused on technical challenges (ie, the accuracy of motion tracking [12,15]), reliability of tracking over long periods of time [16], wearability for patients [17], and the processing of metrics from sensor data [18]. While all of these issues are important to realize the potential of wearable sensor technology, to date there has been little consideration for the needs of therapists and whether this information is useful for the rehabilitation process.…”

Section: Introductionmentioning

confidence: 99%

How Therapists Use Visualizations of Upper Limb Movement Information From Stroke Patients: A Qualitative Study With Simulated Information

Ploderer¹,

Fong²,

Klaic³

et al. 2016

JMIR Rehabil Assist Technol

View full text Add to dashboard Cite

BackgroundStroke is a leading cause of disability worldwide, with upper limb deficits affecting an estimated 30% to 60% of survivors. The effectiveness of upper limb rehabilitation relies on numerous factors, particularly patient compliance to home programs and exercises set by therapists. However, therapists lack objective information about their patients’ adherence to rehabilitation exercises as well as other uses of the affected arm and hand in everyday life outside the clinic. We developed a system that consists of wearable sensor technology to monitor a patient’s arm movement and a Web-based dashboard to visualize this information for therapists.ObjectiveThe aim of our study was to evaluate how therapists use upper limb movement information visualized on a dashboard to support the rehabilitation process.MethodsAn interactive dashboard prototype with simulated movement information was created and evaluated through a user-centered design process with therapists (N=8) at a rehabilitation clinic. Data were collected through observations of therapists interacting with an interactive dashboard prototype, think-aloud data, and interviews. Data were analyzed qualitatively through thematic analysis.ResultsTherapists use visualizations of upper limb information in the following ways: (1) to obtain objective data of patients’ activity levels, exercise, and neglect outside the clinic, (2) to engage patients in the rehabilitation process through education, motivation, and discussion of experiences with activities of daily living, and (3) to engage with other clinicians and researchers based on objective data. A major limitation is the lack of contextual data, which is needed by therapists to discern how movement data visualized on the dashboard relate to activities of daily living.ConclusionsUpper limb information captured through wearable devices provides novel insights for therapists and helps to engage patients and other clinicians in therapy. Consideration needs to be given to the collection and visualization of contextual information to provide meaningful insights into patient engagement in activities of daily living. These findings open the door for further work to develop a fully functioning system and to trial it with patients and clinicians during therapy.

show abstract

“…Zhang et al followed a more exploratory approach, aiming towards a temporally fine-grained motor performance assessment [23]. They used Dynamic Time Warping b to compare movement trajectories of the affected and unaffected arm.…”

Section: Introductionmentioning

confidence: 99%

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

Strohrmann

Labruyère

Gerber

et al. 2013

Journal of NeuroEngineering and Rehabilitation

View full text Add to dashboard Cite

BackgroundRehabilitation services use outcome measures to track motor performance of their patients over time. State-of-the-art approaches use mainly patients’ feedback and experts’ observations for this purpose. We aim at continuously monitoring children in daily life and assessing normal activities to close the gap between movements done as instructed by caregivers and natural movements during daily life. To investigate the applicability of body-worn sensors for motor assessment in children, we investigated changes in movement capacity during defined motor tasks longitudinally.MethodsWe performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation. Every week, the children performed 10 predefined motor tasks. Capacity in terms of quality and quantity was assessed by experts and movement was monitored using 10 ETH Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features such as smoothness of movement were calculated from the sensor data and a regression was used to estimate the capacity from the features and their relation to clinical data. Therefore, the target and features were normalized to range from 0 to 1.ResultsWe achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86 (p<0.05 for all tasks) between our regression estimate of motor task capacity and experts’ ratings across all tasks. We identified the most important features and were able to reduce the sensor setup from 10 to 3 sensors. We investigated features that provided a good estimate of the motor capacity independently of the task performed, e.g. smoothness of the movement.ConclusionsWe found that children’s task capacity can be assessed from wearable sensors and that some of the calculated features provide a good estimate of movement capacity over different tasks. This indicates the potential of using the sensors in daily life, when little or no information on the task performed is available. For the assessment, the use of three sensors on both wrists and the hip suffices. With the developed algorithms, we plan to assess children’s motor performance in daily life with a follow-up study.

show abstract

Beyond the standard clinical rating scales: Fine-grained assessment of post-stroke motor functionality using wearable inertial sensors

Cited by 33 publications

References 10 publications

Measuring changes in gait and vehicle transfer ability during inpatient rehabilitation with wearable inertial sensors

Measuring changes in gait and vehicle transfer ability during inpatient rehabilitation with wearable inertial sensors

How Therapists Use Visualizations of Upper Limb Movement Information From Stroke Patients: A Qualitative Study With Simulated Information

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

Contact Info

Product

Resources

About