TY  - JOUR
AU  - Li, Kenan
AU  - Habre, Rima
AU  - Deng, Huiyu
AU  - Urman, Robert
AU  - Morrison, John
AU  - Gilliland, Frank D
AU  - Ambite, José Luis
AU  - Stripelis, Dimitris
AU  - Chiang, Yao-Yi
AU  - Lin, Yijun
AU  - Bui, Alex AT
AU  - King, Christine
AU  - Hosseini, Anahita
AU  - Vliet, Eleanne Van
AU  - Sarrafzadeh, Majid
AU  - Eckel, Sandrah P
PY  - 2019
DA  - 2019/02/07
TI  - Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data
JO  - JMIR Mhealth Uhealth
SP  - e11201
VL  - 7
IS  - 2
KW  - machine learning
KW  - physical activity
KW  - smartphone
KW  - statistical data analysis wearable devices
AB  - Background: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. Objective: We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. Methods: We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. Results: In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. Conclusions: In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data. 
SN  - 2291-5222
UR  - http://mhealth.jmir.org/2019/2/e11201/
UR  - https://doi.org/10.2196/11201
UR  - http://www.ncbi.nlm.nih.gov/pubmed/30730297
DO  - 10.2196/11201
ID  - info:doi/10.2196/11201
ER  -