There have been a variety of approaches proposed for HAR using acceleration data from smartphones. One of the most popular approaches used are SVM, random forest and decision tree (Wang et al., 2012). These algorithms have been shown to be operative in identifying a wide range of activities with high accuracy.
Using deep learning techniques, in particular convolutional neural networks, is another way that is becoming increasingly common (Chen et al., 2016). CNNs have been shown to be particularly effective in recognizing activities from time-series data, such as acceleration data, and have achieved high accuracy rates in HAR.
Additionally, researchers (Bao and Intille, 2004) have also proposed using other types of sensors, such as the gyroscope and magnetometer, in addition to the accelerometer, to achieve a higher degree of precision in activity recognition.
Another important aspect of HAR is the ability to classify activities with high precision and recall. In recent years, researchers have proposed the use of more sophisticated algorithms such as Random Forest, Random Subspace and Random Kit Algorithms (Nwe, M.T. and Kinshuk, 2016) and Multi-layer Perceptron (MLP) (Chen, Y., Chen, X., & Wang, Y., 2016) to improve the performance of HAR system, These methods have demonstrated superior performance when compared to classic machine learning techniques like decision trees and support vector machines.
Another important aspect of HAR is the use of data from various sources, such as wearable devices, smartphones, and smartwatches. This allows for a more comprehensive understanding of human activity, as well as the ability to recognize activities in different environments and contexts. For example, using data from a smartwatch can provide information on activity recognition during sleep, whereas using data from a smartphone can provide information on activity recognition during daily life. (Ravi, V., & Surya, V., 2016)
However, despite the advancements in technology and algorithms, there are still some limitations in using smartphones for HAR. One of the main limitations is the lack of generalization, as the performance of the system may vary when applied to different populations or environments. Additionally, the use of smartphones for HAR may raise privacy concerns, as the collected data may contain sensitive information. (Kwapisz, J. R., & Moore, S. A., 2011)
Another important aspect of HAR using acceleration data from smartphones is the use of signal processing techniques to pre-process and extract relevant features from the raw acceleration data. One popular method is the use of windowing techniques, where the raw data is divided into overlapping segments, and calculations are done for each section to determine statistical characteristics including mean, standard deviation, and energy (Lara, O., & Reyes, A., 2013).
Another important aspect is the use of machine learning algorithms to classify the activities based on the extracted features. One popular method is the use of ensemble learning, which combines multiple classifiers to improve the overall performance of the system. (Cao, Y., & Liu, Y., 2014)
Additionally, researchers have also proposed using other types of data, such as audio and video data, in combination with acceleration data to improve the performance of the system. (Wang, Y., & Liu, Y., 2015)
However, there are also challenges in using acceleration data from smartphones for HAR, such as dealing with the high dimensionality of the data, and the impact of sensor noise and orientation. (Gao, X., & Liu, Y., 2016)
The utilisation of wearable devices for the purpose of data collection is yet another essential component of HAR that makes use of acceleration data from cellphones. When opposed to the use of smartphones, the collection of data through the utilisation of wearable technologies such as smartwatch and fitness bands can provide a method that is both more continuous and less intrusive. (Zeng, Z., & Chen, H., 2018)
Another important aspect is the use of transfer learning technique to increase the performance of the system. Transfer learning methods can be used to transfer knowledge learned from one dataset to a different dataset, which can be particularly useful when dealing with limited data in certain activities or populations. (Song, Y., & Li, H., 2019)
Additionally, researchers have also proposed using other types of sensors, such as the barometer and heart rate sensors, in addition to the accelerometer, to improve the accuracy of activity recognition. (Yan, L., & Li, H., 2020)
However, there are also challenges in using acceleration data from smartphones and wearable devices for HAR, such as dealing with inter-device variability and ensuring data privacy and security. (Wang, X., & Li, H., 2021)
Researchers have recently suggested employing more sophisticated methods to boost the effectiveness of HAR using smartphone acceleration data. Recurrent neural networks are one such approach used for feature extraction and categorization. RNNs have been shown to be effective in capturing temporal dependencies in the acceleration data, which can be useful for recognizing activities that have a temporal structure, such as walking and running (Wang, L., & Li, H., 2019).
Another technique that has been proposed is the use of Attention-based models to improve the performance of the system. Attention-based models can be used to automatically focus on the most relevant features in the acceleration data, which can improve the accuracy of activity recognition (Zhou, Z., & Li, H., 2020).
Additionally, researchers have also proposed using domain adaptation techniques to enhance the system performance when dealing with data from different sources or populations. Domain adaptation techniques can be used to adapt a model trained on one dataset to a different dataset, which can improve the generalization of the system (Deng, Y., & Li, H., 2021).
However, there are also challenges in using acceleration data from smartphones for HAR, such as dealing with variability in the data, and ensuring data privacy and security. Researchers have proposed the use of techniques such as data augmentation and federated learning to address these challenges (Chen, X., & Li, H., 2021).
"Human activity recognition using convolutional neural networks" by Fang, X., & Yang, X. (2020) published in Knowledge-Based Systems. This paper presents an approach for using convolutional neural network for HAR using smartphone sensors.
"Human activity recognition using deep learning approach" is proposed by Li, Z., Wang, Z., & Guo, Y. (2020) and Zhang, Y., & Li, J. (2022). This papers discuss the use of deep learning based approach for HAR using wearable sensors such as smartwatches and fitness trackers.
"Human activity recognition using recurrent neural networks" by Zhang, L., Wang, Y., & Li, X. (2021) published in Applied Sciences. This paper presents an approach for using recurrent neural networks for HAR using smartphone sensors.
"Human activity recognition using wearable sensors: A review" by Pan, Q., & Chen, Y. (2022) published in IEEE Transactions on Industrial Informatics. This paper reviews recent research on how wearable sensors can be used for HAR.
These papers provide a wealth of information on the current state of research on HAR using acceleration data from smartphones and wearable devices. The use of deep learning approaches has been shown to be effective for improving the performance of HAR, and the use of multi-modal data can further enhance performance. Future research will likely continue to explore the use of these and other approaches for this important area of study.
An experimental evaluation using smartphone accelerometer data refers to a study that uses the acceleration data collected from smartphone sensors to assess the performance of a human activity recognition system. In such evaluations, the accelerometer data is used as the input to the system and the system's output is compared to the actual human activity being performed. This allows researchers to assess the accuracy and reliability of the system in recognizing different types of human activities.
The experimental evaluation can be carried out in a controlled environment or in a real-world setting, depending on the research objectives. In a controlled environment, partakers are requested to accomplish a predefined set of actions, and the accelerometer data is collected and used to assess the system. In a practical setting, the participants carry out their activities naturally, and the accelerometer data is castoff to evaluate the system's capability to recognize these activities in a more realistic scenario.
One important aspect of these evaluations is the selection of human activities to be recognized. Common activities that are often used in experimental evaluations include walking, running, sitting, standing, and climbing stairs. In some studies, more complex activities, such as cycling or driving a car, are also included. The choice of activities to be recognized is crucial as it directly impacts the system's performance and accuracy.
Another important aspect of the experimental evaluation is the choice of algorithms and models for recognizing human activities. There are various algorithms and models available for this purpose. The choice of algorithms and models will depend on the type and complexity of the activities to be recognized, as well as the available computational resources.
The results of the experimental evaluations using smartphone accelerometer data have been promising, with many systems achieving high accuracy rates in recognizing human activities. However, there is still room for improvement, and further research is needed to address issues such as robustness in the face of noise and missing data, scalability, and generalization to different populations and environments.