%A Albert,Mark %A Toledo,Santiago %A Shapiro,Mark %A Koerding,Konrad %D 2012 %J Frontiers in Neurology %C %F %G English %K accelerometer,activity recognition,mobile phone,Parkinson's disease,machine learning %Q %R 10.3389/fneur.2012.00158 %W %L %M %P %7 %8 2012-November-07 %9 Original Research %+ Dr Mark Albert,Rehabilitation Institute of Chicago,Sensory Motor Performance Program,345 E Superior St,Chicago,60611,IL,United States,mark.albert@unt.edu %+ Dr Mark Albert,Northwestern University,Department of Physical Medicine and Rehabilitation,Chicago,United States,mark.albert@unt.edu %# %! Using mobile phones for activity recognition in Parkinson’s patients %* %< %T Using Mobile Phones for Activity Recognition in Parkinson’s Patients %U https://www.frontiersin.org/articles/10.3389/fneur.2012.00158 %V 3 %0 JOURNAL ARTICLE %@ 1664-2295 %X 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.