The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.

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Human activity recognition (HAR) as an emerging technology can have undeniable impacts in several applications, including health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR, which are sensor, image, and WiFi-based, the last one has attracted more attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to recognize daily human activities such as running, walking, and sleeping. These activities affect WiFi signal propagation and can be further used to identify human activities. This paper proposes a Deep Learning (DL) method for activity recognition tasks using WiFi channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a Convolutional Neural Network (CNN) with 2-dimensional convolutional layers for activity recognition. We take advantage of CNN's high accuracy on image classification along with WiFi-based preponderance.The experimental results demonstrate that our proposed approach achieves good performance in HAR tasks.

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers.

The purpose of this study is to determine the effects of the motivational and instructional self-talk and task complexity on the accuracy of forehand top spin of table tennis in advanced players. The 30 male advanced players were divided into 3 groups (2 experimental and 1 control). The task complexity was determined by color of ball and place of table placement. In other words, sequence sending of the ball were changed after two balls and this trend continues. The keywords for motivational self-talk were "I can do" and " I correctly recognize", and for Instructional self-talk "pay attention" and "Close your paddle". Masters et. al test (2008) was used to measure the accuracy of forehand topspin. After the pre-test, subjects took part in 6 training sessions including 20 trails per session. After 48 hours, they participated in post-test. The data were analyzed by paired-samples t-test, one way ANOVA and Tukey post hoc test. Results showed that there is significant difference between the instructional and motivational self-talk in terms of task complexity. These findings suggested that instructional self-talk is the effective variable in performance of tasks that needs high complex decisions and accuracy.