A bag-of-words approach for assessing activities of daily living using wrist accelerometer data

The Physical Activity Questionnaire for Elderly Japanese (PAQ-EJ) is a self-administered physical activity questionnaire for elderly Japanese; the authors report here on its repeatability and direct and indirect validity. Reliability was assessed by repeat administration after 1 month. Direct validation was based on accelerometer data collected every 4 s for 1 month in 147 individuals age 65–85 years. Indirect validation against a 10-item Barthel index (activities of daily living [ADL]) was completed in 3,084 individuals age 65–99 years. The test–retest coefficient was high (r= .64–.71). Total and subtotal scores for lower (transportation, housework, and labor) and higher intensity activities (exercise/sports) were significantly correlated with step counts and durations of physical activity <3 and ≥3 METs (r= .41, .28, .53), respectively. Controlling for age and ADL, scores for transportation, exercise/sports, and labor were greater in men, but women performed more housework. Sex- and ADL- or age-adjusted PAQ-EJ scores were significantly lower in older and dependent people. PAQ-EJ repeatability and validity seem comparable to those of instruments used in Western epidemiological studies.

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Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

Electronics ◽

10.3390/electronics9030509 ◽

2020 ◽

Vol 9 (3) ◽

pp. 509 ◽

Cited By ~ 8

Author(s):

Ivan Miguel Pires ◽

Gonçalo Marques ◽

Nuno M. Garcia ◽

Francisco Flórez-Revuelta ◽

Maria Canavarro Teixeira ◽

...

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Activities Of Daily Living ◽

Mobile Devices ◽

Mobile Device ◽

Daily Living ◽

Accelerometer Data ◽

Pattern Recognition Techniques ◽

Multiple Sensors ◽

Computational Resources

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

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Is Wrist Accelerometry Suitable for Threshold Scoring? A Comparison of Hip-Worn and Wrist-Worn ActiGraph Data in Low-Active Older Adults with Obesity

The Journals of Gerontology Series A ◽

10.1093/gerona/glab347 ◽

2021 ◽

Author(s):

Jason Fanning ◽

Michael E Miller ◽

Shyh-Huei Chen ◽

Carlo Davids ◽

Kyle Kershner ◽

...

Keyword(s):

Older Adults ◽

Activities Of Daily Living ◽

Daily Living ◽

Objective Assessment ◽

Community Dwelling ◽

Stair Climbing ◽

Accelerometer Data ◽

Fast Walking ◽

Community Dwelling Older Adults ◽

Monitor Data

Abstract Background Hip- and wrist-worn ActiGraph accelerometers are widely used in research on physical activity as they offer an objective assessment of movement intensity across the day. Herein we characterize and contrast key structured physical activities and common activities of daily living via accelerometry data collected at the hip and wrist from a sample of community-dwelling older adults. Methods Low-active, older adults with obesity (age 60+ years) were fit with an ActiGraph GT3X+ accelerometer on their non-dominant wrist and hip before completing a series of tasks in a randomized order, including sitting/standing, sweeping, folding laundry, stair climbing, ambulation at different intensities, and cycling at different intensities. Participants returned a week later and complete the tasks once again. Vector magnitude counts/second were time-matched during each task and then summarized into counts/minute (CPM). Results Monitors at both wear locations similarly characterized standing, sitting, and ambulatory tasks. A key finding was that light home chores (sweeping, folding laundry) produced higher and more variable CPM values than fast walking via wrist ActiGraph. Regression analyses revealed wrist CPM values were poor predictors of hip CPM values, with devices aligning best during fast walking (R 2=.25) and stair climbing (R 2=.35). Conclusion As older adults spend a considerable portion of their day in non-exercise activities of daily living, researchers should be cautious in the use of simply acceleration thresholds for scoring wrist-worn accelerometer data. Methods for better classifying wrist-worn activity monitor data in older adults are needed.

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