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Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification
| Content Provider | Scilit |
|---|---|
| Author | Ellis, Katherine Kerr, Jacqueline Godbole, Suneeta Staudenmayer, John Lanckriet, Gert |
| Copyright Year | 2016 |
| Description | Journal: Medicine & Science In Sports & Exercise Purpose Accelerometers are a valuable tool for objective measurement of physical activity (PA). Wrist-worn devices may improve compliance over standard hip placement, but more research is needed to evaluate their validity for measuring PA in free-living settings. Traditional cut-point methods for accelerometers can be inaccurate and need testing in free living with wrist-worn devices. In this study, we developed and tested the performance of machine learning (ML) algorithms for classifying PA types from both hip and wrist accelerometer data. Methods Forty overweight or obese women (mean age = 55.2 ± 15.3 yr; BMI = 32.0 ± 3.7) wore two ActiGraph GT3X+ accelerometers (right hip, nondominant wrist; ActiGraph, Pensacola, FL) for seven free-living days. Wearable cameras captured ground truth activity labels. A classifier consisting of a random forest and hidden Markov model classified the accelerometer data into four activities (sitting, standing, walking/running, and riding in a vehicle). Free-living wrist and hip ML classifiers were compared with each other, with traditional accelerometer cut points, and with an algorithm developed in a laboratory setting. Results The ML classifier obtained average values of 89.4% and 84.6% balanced accuracy over the four activities using the hip and wrist accelerometer, respectively. In our data set with average values of 28.4 min of walking or running per day, the ML classifier predicted average values of 28.5 and 24.5 min of walking or running using the hip and wrist accelerometer, respectively. Intensity-based cut points and the laboratory algorithm significantly underestimated walking minutes. Conclusions Our results demonstrate the superior performance of our PA-type classification algorithm, particularly in comparison with traditional cut points. Although the hip algorithm performed better, additional compliance achieved with wrist devices might justify using a slightly lower performing algorithm. |
| Related Links | http://europepmc.org/articles/pmc4833514?pdf=render https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833514/pdf |
| Ending Page | 940 |
| Page Count | 8 |
| Starting Page | 933 |
| ISSN | 01959131 |
| e-ISSN | 15300315 |
| DOI | 10.1249/mss.0000000000000840 |
| Journal | Medicine & Science In Sports & Exercise |
| Issue Number | 5 |
| Volume Number | 48 |
| Language | English |
| Publisher | Ovid Technologies (Wolters Kluwer Health) |
| Publisher Date | 2016-05-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Medicine & Science In Sports & Exercise Hidden Markov Model Physical Activity |
| Content Type | Text |
| Resource Type | Article |
| Subject | Orthopedics and Sports Medicine Physical Therapy, Sports Therapy and Rehabilitation Sports Science |
