Using Mobile Phones for Activity Recognition in Parkinson’s Patients

Sensory Motor Performance Program, Rehabilitation Institute of Chicago Chicago, IL, USA
Frontiers in Neurology 11/2012; 3:158. DOI: 10.3389/fneur.2012.00158
Source: PubMed


Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the following activities of 18 healthy subjects and eight patients with Parkinson's disease: walking, standing, sitting, holding, or not wearing the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using cross validation across all samples we are able to correctly identify 96.1% of the activities of healthy subjects and 92.2% of the activities of Parkinson's patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson's patients, the percent correct lowers to 60.3%, due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise cross validation, identifying healthy subject activities with 86.0% accuracy and 75.1% accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities.

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    • "Second, the HMM module within our framework effectively integrates together the activity and floor inference. Previous studies either recognized human activity from inertial motion unit sensors [5], [7], or inferred the floors from barometric sensors [12], [13]. To our best knowledge, the activity and floor recognition have not been considered at the same time. "
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    • "Patients with Parkinson's disease wore three body-fixed inertial sensors which were used to accurately identify activities including walking, sitting, standing, lying, sit to stand, and stand to sit transitions (Salarian et al., 2007). It has also been shown that a mobile phone placed in a pocket can be used to recognize activities in people with Parkinson's disease (Albert et al., 2012b). Each of these systems can quantify physical activity, but they require participants to wear sensors in a fixed location, and generally wearing more sensors leads to more accurate tracking (Bao and Intille, 2004). "
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