Devising a novel visible light based low-cost ultra-low-power gesture recognition system

2017 ◽  
Author(s):  
Tusher Chakraborty ◽  
Md. Nasim ◽  
Sakib Md. Bin Malek ◽  
Md. Taksir Hasan Majumder ◽  
Md. Samiul Saeef ◽  
...  
Keyword(s):  
Low Power ◽  
Visible Light ◽  
Gesture Recognition ◽  
Low Cost ◽  
Recognition System ◽  
Ultra Low Power

scholarly journals CrossGR

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Xinyi Li ◽  
Liqiong Chang ◽  
Fangfang Song ◽  
Ju Wang ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
User Involvement ◽  
Recognition System ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Target User ◽  
Order Of Magnitude ◽  
Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

Low-cost finger gesture recognition system for disabled and elderly people

2017 ◽  
Author(s):  
Tusher Chakraborty ◽  
Md. Nasim ◽  
Sakib Md Bin Malek ◽  
Md. Taksir Hasan Majumder ◽  
Mohammed Samiul Saeef ◽  
...  
Keyword(s):  
Elderly People ◽  
Gesture Recognition ◽  
Low Cost ◽  
Recognition System

Low-cost intelligent static gesture recognition system

2016 ◽  
Author(s):  
Harini Sekar ◽  
R Rajashekar ◽  
Gosakan Srinivasan ◽  
Priyanka Suresh ◽  
Vineeth Vijayaraghavan
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Recognition System ◽  
Static Gesture Recognition

scholarly journals MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers

2007 ◽  
Vol 2007 ◽  
pp. 1-11 ◽  
Author(s):  
Elisabetta Farella ◽  
Luca Benini ◽  
Bruno Riccò ◽  
Andrea Acquaviva
Keyword(s):  
Low Power ◽  
Motion Capture ◽  
Gesture Recognition ◽  
Tracking System ◽  
Low Cost ◽  
Dynamic Assessment ◽  
Point Of View ◽  
Test Bed ◽  
Natural Interaction ◽  
Robust Detection

Human-computer interaction (HCI) and virtual reality applications pose the challenge of enabling real-time interfaces for natural interaction. Gesture recognition based on body-mounted accelerometers has been proposed as a viable solution to translate patterns of movements that are associated with user commands, thus substituting point-and-click methods or other cumbersome input devices. On the other hand, cost and power constraints make the implementation of a natural and efficient interface suitable for consumer applications a critical task. Even though several gesture recognition solutions exist, their use in HCI context has been poorly characterized. For this reason, in this paper, we consider a low-cost/low-power wearable motion tracking system based on integrated accelerometers called motion capture with accelerometers (MOCA) that we evaluated for navigation in virtual spaces. Recognition is based on a geometric algorithm that enables efficient and robust detection of rotational movements. Our objective is to demonstrate that such a low-cost and a low-power implementation is suitable for HCI applications. To this purpose, we characterized the system from both a quantitative point of view and a qualitative point of view. First, we performed static and dynamic assessment of movement recognition accuracy. Second, we evaluated the effectiveness of user experience using a 3D game application as a test bed.

scholarly journals WIRELESS REAL TIME PROPORTIONAL CONTROL SYSTEM

2013 ◽  
pp. 15-20
Author(s):  
UJJWALA G. BORATE ◽  
PROF. R.T. PATIL
Keyword(s):  
Low Power ◽  
Real Time ◽  
Low Cost ◽  
Ultra Low Power ◽  
Proportional Control ◽  
Star Network ◽  
Precise Control ◽  
Zigbee Technology ◽  
Temperature And Humidity ◽  
Transceiver Module

This system provides low power consuming and low cost wireless sensor network. This system provides a real time temperature and humidity. It also gives proportional control action. This system consists of TI’s MSP430 microcontroller which consumes ultra low power and improves the overall system performance. The Sensorion’s SHT 11 sensor is used to measure temperature and humidity. Sensor SHT 11 consumes low power and gives the fully calibrated digital output. Zigbee technology is used for wireless communication. Zigbee is low power consuming transceiver module. It operates within the ISM 2.4 GHz frequency band. AT and API command modes configure module parameters. RF data rate is 250 kbps. To achieve the proportional control triac and MOC 3022 are used. The star network topology is implemented. The temperature of earth goes on increasing due to global warming, deforestation, pollution, etc. Due to this the temperature of atmosphere also increases which is harmful and dangerous for many systems. This system provides precise control of temperature and humidity in Green House, Art Galleries and Industries.

scholarly journals Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1401
Author(s):  
Haq Nawaz ◽  
Ahsen Tahir ◽  
Nauman Ahmed ◽  
Ubaid U. Fayyaz ◽  
Tayyeb Mahmood ◽  
...  
Keyword(s):  
Low Power ◽  
Indoor Localization ◽  
Low Cost ◽  
Smart Homes ◽  
Indoor Positioning ◽  
High Accuracy ◽  
Positioning System ◽  
Telemetry Data ◽  
Ultra Low Power ◽  
Indoor Positioning System

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

scholarly journals Low-Cost and Ultra-Low-Power Consuming RTUs for Use in IoT Systems

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Low Power ◽  
Low Cost ◽  
General Packet Radio Service ◽  
Ultra Low Power ◽  
Packet Radio

This paper presents the design and realization of low-cost and ultra-low-power consuming remote transfer units (RTUs), working as communication gateways for collecting, aggregating, and forwarding IoT data to information centers (servers) in the cloud for further processing and data mining. Two types of RTUs, targeting different application scenarios and utilizing different communication standards, were designed – one, based on the General Packet Radio Service (GPRS) standard, and another – on the NarrowBand Internet of Things (NB-IoT) standard. The developed RTUs were experimentally tested and their use was successfully demonstrated in different IoT systems.

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