Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in real-time and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach$0.06m$.

Inattentive driving is one of the high-risk factors that causes a large number of traffic accidents every year. In this article, we aim to detect driver inattention leveraging on large-scale vehicle trajectory data while at the same time explore how do these inattentive events affect driver behaviors and what following reactions they may cause, especially, for commercial vehicles. Specifically, the proposed system targets four most commonly occurring critical inattentive events, including smoking, phone call, turning back, and yawning. By applying a deep convolutional neural network (CNN) (Inception v3) with two data augmentation routines—Mixup and synthetic minority oversampling technique (Smote), we are able to balance the training data distribution and improve the generalization of the classification model. Then, based on the output derived from the inattention detection combining with point of interest (POI) and climate data, a long short-term memory (LSTM)-based model is deployed to predict driver upcoming abnormal operations on road (due to inattention) which may result in potential dangerous driving conditions, such as sudden acceleration/deceleration, aggressive left/right lane change, etc. To evaluate our proposed system, we collect more than 120000 real-world driving traces from over 200 drivers. The experimental results show that our model achieves a weight accuracy (WA) of 92.27% for inattentive driving detection and a WA of 91.67% for abnormal driving prediction, demonstrating its great potential of shaping good driving habits and promoting road safety.

Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%.