# Efficient Human Activity Recognition Using Machine Learning and Wearable Sensor Data
## 1 Introduction
With the advancement of accelerometer technology, sensors have become smaller, more portable, and are now widely utilized for monitoring human movement and health. These compact sensors are increasingly integrated into medical monitoring devices and outdoor sports equipment, offering enhanced versatility and practicality across a wide range of applications [4]. In the early stages, researchers generally used accelerometer signals to study specific human motion states, particularly for fall detection [6]. [PERSON] et al. designed a system utilizing over 20 different types of sensors, including two accelerometers placed on the chest and wrist, to recognize daily human activities. However, the large number of sensors made the system cumbersome and significantly reduced comfort. As a result, recent studies have shifted toward using fewer sensors to study human motion [7]. [PERSON] et al. used a single accelerometer placed on the chest and designed a system that integrated data collection and storage, capable of recognizing five different movement states with high accuracy. However, in multi-sensor fusion applications, synchronizing and standardizing data from different sensors present significant challenges. The frequency and resolution of data collected by various sensors may differ, and effectively aligning and standardizing these data to ensure consistency and accuracy remains a critical technical issue that needs to be addressed [8]. To avoid impacting user comfort, some researchers have embedded sensors into everyday items. For instance, [PERSON] and colleagues installed sensors in a watch [9], and [PERSON] placed sensors on a cane to detect falls by measuring the cane's angular velocity changes [10]. Many researchers have also explored various movement statesin activity recognition. [PERSON] utilized smartphones to recognize various daily activities such as smoking, eating, drinking coffee, walking, and cycling [11]. [PERSON] proposed a wearable inertial sensor network to recognize 10 common household activities and 11 sports activities, achieving a recognition accuracy of 95% [12]. [PERSON] analyzed fused data from smartphone sensors of 30 subjects and recognized 15 different activities [13]. Since the raw data collected by sensors are complex and high-dimensional, it is quite challenging to directly extract meaningful information. Machine learning algorithms, however, are proficient in handling such complex data. They can automatically learn the patterns and relationships within the sensor data and are applied to develop accurate models for activities such as activity recognition and fall detection. By combining these two aspects, a large amount of raw sensor data can be transformed into useful information, filling the existing research gaps and improving the accuracy and reliability of human movement analysis in real-world scenarios. For example, [PERSON] and [PERSON] used k-nearest neighbors (KNN) classification and k-means clustering to recognize walking, running, sitting, and standing [14]. San-Segundo proposed a human motion state sensing system based on a hidden Markov model to classify six human motion states: walking, climbing stairs, descending stairs, sitting, standing, and lying down [15]. However, these machine learning algorithms were single classifiers. They may suffer from lower recognition accuracy when used in real-world applications. In this work, by combining data preprocessing methods, we carried out feature extraction on time series data in the time domain and frequency domain. Moreover, we established a majority decision model, which integrated multiple machine learning methods and the voting mechanism to effectively recognize human activities. It is more diverse in feature extraction, deriving multi-dimensional complex features in the time domain, frequency domain, etc., which significantly improves the ability to accurately distinguish complex activities and similar activities. Additionally, it can process data in real-time and provide feedback on the recognition results of human activities.