Leveraging Smartphone Sensor Data and Machine Learning Model for Human Activity Recognition and Fall Classification.

This study investigates the application of Machine Learning (ML) algorithms to classify human physical activities and detect different types of falls using smartphone sensor data. Smartphones equipped with various sensors offer new opportunities for healthcare and human activity monitoring, which are crucial for health and well-being, aligning with the United Nations' sustainable development goals (SDG). The study used the "Sensor Data Collector" Android application to collect smartphone sensor data. The data collection experiments involve ten healthy participants engaged in diverse physical activities and different fall events, utilizing accelerometers and gyroscope sensors. Different types of fall detection include forward, backward, left lateral, and right lateral fall, which are important from a seriousness of health point of view. Participants placed their smartphones in the front pocket of their trousers to ensure a fixed position during the data collection process. The collected datasets are preprocessed using a moving average filter to denoise and smoothen the data. Different statistical features and extracted and ML algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and K-Nearest Neighbors (KNN), are employed to create robust classifiers. SVM and RF performed exceptionally well in classifying normal physical activities, achieving a 99.09% accuracy. SVM excelled in daily walking activity classification with a 99.59% accuracy, while the K-NN model performed best for crucial different types of fall classification, achieving an accuracy of 85.71%. This research emphasizes the potential of ML models for accurate physical activity and various types of fall classification, expanding the horizons of smartphone-based healthcare applications and aligning with sustainable development goals. [ABSTRACT FROM AUTHOR]