# SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning
###### Abstract  
Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF; and AdaBoost. h +
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[31][PERSON] SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning Algorithms 20 be standalone devices that people carry with them or wearables that may be embedded in smart devices, such as smartphones, smartwatches, etc. People carry these smart devices with them while performing daily activities, such as walking, running, standing, eating, etc. Sensors such as a gyroscope, accelerometer, global positioning system (GPS), microphone, magnetometer, and barometer can sense and record a user's physical condition, location change, velocity change, elevation, and other characteristics and activities. Smartphones, smartwatches, smart healthcare bands, cameras, infrared devices, etc. can record various parameters that can help to understand our environment in a way that enhances our ability to make smart decisions. Therefore, HAR has become critical in a wide arrange of important application domains. For instance, it has been used for the elderly and in child care [11], to improve exercise performance in healthcare [12], to optimally recommend the mode of content delivery in distance learning [13], for entertainment, security, and surveillance applications [14], and to monitor social distancing for COVID-19 pandemic prevention [15]. The data used for human activity recognition include numerical data (e.g., location coordinates and magnetic field coordinates) and visual data, such as images and videos. A significant challenge in processing visual data for HAR requires extensive communication and computation resources in terms of CPU, storage, network bandwidth, specialized hardware, etc. [16] On the other hand, location coordinates data used for HAR are less resource-demanding than visual HAR data. Machine learning plays a key role in developing our understanding of HAR data and applying the acquired knowledge to various application domains.