A Machine Learning Approach for the Classification of Falls and Activities of Daily Living in Agricultural Workers

Abstract

Population aging is a global trend, and the highest proportion of elderly people in the workforce per unit of population is found in agricultural areas. However, few systematic studies have been conducted on farmer falls in the field of agricultural machinery. This study focuses on the application of classification methods for monitoring devices to detect fall/nonfall movements of farmworkers, where agricultural biomechanical factors are considered in detecting activities of daily living. In this study, we recorded and analyzed original acquisition datasets of signals obtained from two accelerometers and one gyroscope for 40 healthy individuals who performed various falls and activities of daily living (ADLs). Spatial characteristics were used to train the machine-learning classifiers to distinguish between fall and non-fall events. Supervised machine learning experiments evaluated the effectiveness of the proposed approach: the k-nearest neighbors (kNN) and support vector machine (SVM) algorithms achieved roc auc-scores of 0.999 in distinguishing falls and ADLs (binary-class classification). Moreover, an artificial neural network (ANN) classifier showed the highest performance in terms of classification roc auc-scores of 1.0. The evaluation metric demonstrated the highest performance in the analysis and evaluation of the signal obtained from the S2 acceleration sensor with a measurement range of ±16 g. The proposed SVM classifier evaluations showed a 0.988 roc auc-score for sensor tests in multi-class classification, along with the highest performance in terms of the F1-score and Matthews Correlation Coefficient (MCC) over 84% in the multi-class classification model for distinguishing each of ADLs and Fall using ±16 g acceleration sensor.

Keywords

• Agricultural worker
• fall-detection
• human behavior recognition
• machine learning
• wearable sensor