Abstract
Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects’ movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects’ movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen’s Kappa, corrected kappa, Krippendorff’s alpha and Fleiss’ kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.
Highlights
The share of people aged 65 years and over, among the world’s dependents, has doubled since the mid-1960s, reaching 20% in 2015
We have compiled a comprehensive dataset of representative activities from an independently living, older adult population recorded using two task-based protocols in a laboratory setting and a free-living setting in the participants’ home environment. This dataset is suitable for the validation of existing activity classification algorithms and will allow for the development of new activity classification algorithms using the harvested raw inertial sensor data
We have described the development and collection of a dataset that is suitable for validation of existing, and development of new, activity classification algorithms
Summary
The share of people aged 65 years and over, among the world’s dependents, has doubled since the mid-1960s, reaching 20% in 2015. Projections estimate that by 2050, older persons will account for 36% of people in the dependent age group worldwide [1]. With this projected shift in population demographics, increased demand will be placed on national health care services and budgets. Systems (MEMS) technology has stimulated the advancement of ubiquitous body-worn inertial sensors, facilitating the accurate measurement of body-segment kinematics. These MEMS-based inertial sensors consist of a seismic mass suspended using supporting springs, etched into the silicon layer of miniature integrated circuits. Movement of the mass is governed by the combination of Hook’s Law and Newton’s
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