Tracking Human Motion Direction With Commodity Wireless Networks

Rahaman, Habibur; Dyo, Vladimir

doi:10.1109/jsen.2021.3111132

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…In recent years, many novel wearable HAR techniques and systems have been reported in the literature [6]. These HAR systems mainly improve the performance of the system by introducing new sensing techniques and mounting sensors on new body part locations [7] or combining with wireless communication technologies, such as WIFI, Bluetooth [8], Radio Frequency Identification (RFID), and ZigBee [9], [10]. Wearable HAR systems can be divided into two categories: single-sensor systems and multi-sensor systems.…”

Section: Introductionmentioning

confidence: 99%

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Zhang

Liu

Meng

et al. 2022

Measurement Science Review

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The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.

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Section: Introductionmentioning

confidence: 99%

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Zhang

Liu

Meng

et al. 2022

Measurement Science Review

View full text Add to dashboard Cite

show abstract

“…In recent years, the occurrence of insecurity in residential and commercial buildings has skyrocketed [15]. Detecting when a person leaves [1] or enters a room is critical for creating a safe living environment for people and their belongings. There are numerous [2] motion detection options, each of which is influenced by environmental factors, performance requirements, and technology availability [2].…”

Section: Introductionmentioning

confidence: 99%

Development of a Smart Motion Detection Device for Home and Office Security Applications

Sounder¹,

Aina²,

Akinte³

2022

IJRASET

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The aim of this paper is to develop, implement and test an effective and practical working solution to a smart motion detector for LPWAN technologies using appropriate software and hardware platform. The smart motion detection system consists of a Raspberry Pi, an LCD display, PIR sensors, and wires. The hardware design collaborates with Python programming by being installed on a Raspberry Pi, and it is a key function to detect who is entering a room. The proposed smart motion detector will be able to detect whether a person is leaving or entering a room by analyzing signals from multiple motion sensors. The research carried out a comparative analysis of LoRA, SigFox, NB-IOT Low Power Wide Area Network technologies which include the principle of operation, characteristics, hardware, and make recommendation for the most suitable technology. The remote status update function implementation was done where the system sends the motion events to the cloud over a wireless interface using MQTT protocol. The evaluation which was based on extensive simulation demonstrated the detection of each sensor. Keywords: Smart motion detector, Raspberry pi, Python coding, PIR sensors, LPWAN technologies

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“…Sign language recognition systems based on multiple methods can break the barrier that exists between deaf people and people who do not know sign language. For example, some sign language recognition systems use cameras [5][6][7][8][9] or Kinect [10,11] to capture sign language gesture data literature. However, the data acquisition equipment is affected by the lighting conditions, while the camera captures data in such a way that violates the privacy of the person.…”

Section: Introductionmentioning

confidence: 99%

Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

Hong-hong

Feng

Hao

et al. 2022

Wireless Communications and Mobile Computing

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Artificial intelligence and Internet of Things (IoT) devices are experiencing explosive growth. Currently, the commonly used gesture recognition methods are difficult to deploy and expensive, so this paper uses the Channel State Information (CSI) for Chinese sign language recognition. Aiming at the problems of current gesture recognition methods, such as strong personnel dependence, high computational resource consumption, and low robustness, we proposed a Chinese sign language gesture recognition method named Air-CSL. In this method, the Local Outlier Factor (LOF) removal algorithm and the Discrete Wavelet Transform (DWT) are used to reduce the noise in the data, and the subcarriers that best represent the gesture data are selected by principal component analysis. After denoising, mathematical statistics were extracted from the gesture waveform as the eigenvalues, and the features were fused by the Deep Restricted Boltzmann Machine (DBM). Finally, the result of gesture classification and recognition is obtained by the Gated Recurrent Unit (GRU). In this way, the prediction model realizes as well as the classification of sign language gestures. The results show that the proposed method can effectively recognize Chinese sign language gestures of different people in different environments and has good robustness.

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Tracking Human Motion Direction With Commodity Wireless Networks

Cited by 5 publications

References 26 publications

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Development of a Smart Motion Detection Device for Home and Office Security Applications

Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

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