The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review

Abstract

Purpose

To address the gap in the literature and clarify the expanding role of wearable sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) sensor-based assessment and sensor-based treatment.

Materials and methods

The guideline outlined by the preferred reporting items for systematic reviews and meta-analysis extension for scoping reviews was used to complete this scoping review. Information pertaining to participant demographics, sensory information, data collection, data processing, data analysis, and study results were extracted from the studies for analysis and synthesis.

Results

We included 43 articles in the final review. We organized the results into assessment and treatment categories. The included articles used wearable sensors to identify UE functional motion, categorize motor impairment/activity limitation, and quantify real-world use. Wearable sensors were also used to augment UE training by triggering sensory cues or providing instructional feedback about the affected UE.

Conclusions

Sensors have the potential to greatly expand assessment and treatment beyond traditional clinic-based approaches. This capability could support the quantification of rehabilitation dose, the nuanced assessment of impairment and activity limitation, the characterization of daily UE use patterns in real-world settings, and augment UE training adherence for home-based rehabilitation.

IMPLICATIONS FOR REHABILITATION

• Sensor data have been used to assess UE functional motion, motor impairment/activity limitation, and real-world use.

• Sensor-assisted treatment approaches are emerging, and may be a promising tool to augment UE adherence in home-based rehabilitation.

• Wearable sensors may extend our ability to objectively assess UE motion beyond supervised clinical settings, and into home and community settings.

Keywords:

• Wearable sensors
• accelerometers
• IMU
• upper extremity
• stroke
• sensor-based assessment
• sensor-assisted treatment

Disclosure statement

The authors declare that there is no conflict of interest.

Additional information

Funding

This work was supported by an AHA AWS Postdoctoral Training Grant 19AMTG35210398 (AP) and NIH R01 NS110696, R01 LM013316, K02 NS104207 (HMS).