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Updated: Mar 18, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

A novel method of using accelerometry for upper limb FES control.

Mingxu Sun¹, Laurence Kenney¹, Christine Smith²

¹Centre for Health Sciences Research, University of Salford, Salford M6 6PU, UK.

Medical Engineering & Physics

|July 6, 2016

Summary

This summary is machine-generated.

This study introduces a new accelerometer method for controlling upper limb functional electrical stimulation (FES). The algorithm accurately measures arm angles, showing promise for stroke rehabilitation and human movement applications.

Keywords:

Accelerometer Body segment angle Functional electrical stimulation control Functional tasks Upper limb rehabilitation

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Human Movement Analysis

Background:

Functional electrical stimulation (FES) is crucial for restoring upper limb function in individuals with neurological impairments.
Accurate measurement of body segment angles is essential for effective FES control.
Existing methods for angle measurement can suffer from sensitivity and error issues, particularly during dynamic movements.

Purpose of the Study:

To develop and validate a novel algorithm using a 3-axis accelerometer for precise upper limb segment angle calculation.
To integrate this algorithm into a state-machine controller for real-time FES control during functional tasks.
To assess the accuracy of the proposed method compared to marker-based motion capture.

Main Methods:

A novel algorithm was developed to calculate the angle between the accelerometer x-axis and the gravity vector, addressing limitations of existing methods.
The algorithm was implemented in a state-machine controller for real-time FES control.
Experimental validation involved two participants with post-stroke upper limb impairments performing FES-assisted tasks.
Angles derived from the accelerometer were compared against those obtained from reflective marker data.

Main Results:

The accelerometer-based algorithm demonstrated high accuracy in capturing upper limb segment angles.
After accounting for coordinate misalignment, mean errors ranged from 1.4° to 2.9° across tasks and subjects.
The method proved robust, minimizing errors even with significant true acceleration relative to gravity.

Conclusions:

The novel accelerometer-based approach offers a promising solution for accurate upper limb angle measurement in FES control.
This method has significant potential for enhancing FES-assisted rehabilitation for stroke survivors.
The algorithm's applicability extends to other human movement applications requiring precise angle detection under specific conditions.