Abstract

The upper limb activity of twenty unilateral upper limb myoelectric prosthesis users and twenty anatomically intact adults were recorded over a 7-day period using two wrist worn accelerometers (Actigraph, LLC). This dataset reflects the real-world activities of the participants during their normal day-to-day routines. Participants included students, working adults, and retirees recruited from across the United Kingdom. This dataset offers a potential wealth of knowledge into a poorly understood cohort. The raw unprocessed data files and the activity count data exported from the Actilife software are provided. We also provide a non-wear algorithm developed for the removal of prosthesis non-wear periods and resulting activity count data corresponding to prothesis wear periods. Finally, we have included the transposed activity diaries provided by the participants. Analysis to date has primarily involved assessment of the symmetry of upper limb activity, however, there is potential to undertake additional analysis such as understanding the differences in the way a prosthesis is used compared to an anatomical arm.

Highlights

  • Background & SummaryStatistics relating to the prevalence of upper limb absence and provision of prostheses are poor[1]

  • For a person with unilateral upper limb absence, over-reliance on the intact side of the body can lead to overuse injuries[3,4,5]

  • Many of the current methods for evaluating the use of upper limb prostheses are time consuming for clinicians, and recent research has shown that the results of upper limb assessments within labs/clinics may not correlate with how a prosthesis is used in the real-world[6]

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Summary

Background & Summary

Statistics relating to the prevalence of upper limb absence and provision of prostheses are poor[1]. By making the full upper limb activity dataset publicly available through figshare[11] we present others with opportunities to undertake their own analysis In this data-descriptor we provide the information required for additional analysis. In the supplementary material to our earlier Scientific Reports paper[6], we described the development of this non-wear algorithm, which removes prosthesis non-wear periods based on the accelerometer signals In this data descriptor we provide the necessary detail to allow both for the replication of our analysis and the development of a more advanced non-wear algorithm. Please note that at present we have only undertaken detailed testing[6] of our algorithm using 60 s epoch data, but all of the information required to run analyses using different epoch lengths are provided in the upper limb activity dataset[11]

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