# Efficient Human Activity Recognition Using Machine Learning and Wearable Sensor Data
## 4 Results and Discussion
In this work, in addition to the basic time-domain features, we also extracted frequency-domain features from the sensors, which enhances the recognition accuracy. A single  
Figure 1: Confusion matrix of the majority decision model. machine learning classification model may suffer from classification bias due to its inherent limitations. To address this, we introduce the concept of majority voting and construct a majority decision model based on multiple machine learning classifiers. By adhering to the principle of the majority rules, the test set undergoes a voting process, which significantly improves the model's accuracy and robustness. The classification and recognition strategy integrates data preprocessing, feature extraction, and optimization, with the goal of enhancing both the accuracy and efficiency of data analysis. However, it is important to note that only a limited number of feature types were selected for human activity state recognition, and only a small number of activity types were identified. During data processing, only acceleration was used as the feature type. Since many feature types change during human motion, it is worth considering additional feature types for more accurate recognition of different activities. Furthermore, the number of sensors used for analyzing human motion posture was relatively small, which only allowed for a rough representation of human posture. Using more sensors could enable more precise recognition of human motion. Additionally, improvements to human motion models are necessary, as the human body structure and motion system are highly complex and cannot be accurately simulated by simple models. Issues such as the degrees of freedom and constraints of body limbs require further in-depth research. When comparing with analogous research, by referencing the findings of [PERSON] et al. in their study on the comparison of multiple feature-learning approaches, where a hybrid deep-learning architecture integrating convolutional neural networks and long short-term memory networks was utilized to capture temporal dependencies within the data, it becomes evident that our majority decision model encounters drawbacks in dealing with intricate activity patterns [30]. Specifically, it is likely to exhibit suboptimal performance when recognizing activities characterized by complex action sequences. Concurrently, in contrast to the research of [PERSON] et al. regarding the recognition of activities of daily living using a transformer-based model, since our majority decision model lacks an attention mechanism to portray global dependencies, its recognition accuracy is likely to be comparatively low in scenarios involving the identification of continuous activities that rely on activity-context dependencies [31]. Meanwhile, in this study, the relatively small dataset size involving only 10 volunteers can affect the generalization and potential overfitting of the model. There may be some unique movement patterns in the general population that are not represented in this limited dataset, so the model may fail to recognize these patterns. A larger dataset is more likely to capture these nuances across a broader range of individuals. When applied to new individuals or real-world scenarios beyond the scope of these 10 volunteers, the model may perform poorly, thus limiting its generalization. Also, the small dataset provides fewer learning examples for the model, and the model may over-emphasize these features and regard them as universal, resulting in overfitting. Therefore, to improve generalization and reduce overfitting, a larger dataset should be collected in subsequent research.