Human gestures have been considered as one of the important human-computer interaction modes. With the fast development of wireless technology in urban Internet of Things (IoT) environment, Wi-Fi can not only provide the function of high-speed network communication but also has great development potential in the field of environmental perception. This paper proposes a gesture recognition system based on the channel state information (CSI) within the physical layer of Wi-Fi transmission. To solve the problems of noise interference and phase offset in the CSI, we adopt a model based on CSI quotient. Then, the amplitude and phase curves of CSI are smoothed using Savitzky-Golay filter, and the one-dimensional convolutional neural network (1D-CNN) is used to extract the gesture features. Then, the support vector machine (SVM) classifier is adopted to recognize the gestures. The experimental results have shown that our system can achieve a recognition rate of about 90% for three common gestures, including pushing forward, left stroke, and waving. Meanwhile, the effects of different human orientation and model parameters on the recognition results are analyzed as well.
Parkinson's disease (PD) is a very common neurodegenerative disease that occurs mostly in the elderly. There are many main clinical manifestations of PD, such as tremor, bradykinesia, muscle rigidity, etc. Based on the current research on PD, the accurate and convenient detection of early symptoms is the key to detect PD. With the development of microelectronic and sensor technology, it is much easier to measure the barely noticeable tremor in just one hand for the early detection of Parkinson's disease. In this paper, we present a smart wearable device for detecting hand tremor, in which MPU6050 (MIDI Processing Unit) consisting of a 3-axis gyroscope and a 3-axis accelerometer is used to collect acceleration and angular velocity of fingers. By analyzing the time of specific finger movements, we successfully recognized the tremor signals with high accuracy. Meanwhile, with Bluetooth 4.0 (Bluetooth Low Energy, BLE) and networking terminal ability, tremor data can be transferred to a monitoring device in real time with extremely low energy consumption. The experimental results have shown that the proposed device (smart ring) is convenient for long-term tremor detection which is vital for early detection and treatment for Parkinson's disease.
Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder’s motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions.
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