Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data

Abstract

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.

Human Action Recognition Based on Hierarchical Multi-Scale Adaptive Conv-Long Short-Term Memory Network

“…Earlier research works on HAR were mainly based on traditional machine learning methods such as the Random Forest (RF), Support Vector Machine (SVM), and Hidden Markov Model (HMM). Gomes et al [31] compared the performances of three classifiers: SVM, RF, and KNN. Kasteren et al [32] proposed a sensor that can automatically recognize actions and data labeling system; they demonstrated the performance of a HMM in recognizing actions.…”

Cross-Domain Classification of Physical Activity Intensity: An EDA-Based Approach Validated by Wrist-Measured Acceleration and Physiological Data

“…For several years, the availability of wearable technologies has stimulated the research on out-of-the-laboratory and automatic PA intensity classification approaches, which should be easy to apply, without requiring any specific calibration or data input by the user. Many studies are based on exploiting Inertial Measurement Units (IMUs) [12][13][14][15], or surface ElectroMyoGraphy (sEMG) sensors [16,17]. These types of sensors, however, only capture external workloads, which is useful to calculate the absolute intensity of the exerted PA, by detecting the electric activity of muscles [13,18].…”