Wearable Resistive-based Gesture-Sensing Interface Bracelet

Chen, Yujia; Liang, Xiangpeng; Assaad, Maher; Heidari, Hadi

doi:10.1109/ucet.2019.8881832

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…2022, 12, x FOR PEER REVIEW 2 of 20 pattern with a smaller number of electrodes and, hence, the simplest hardware system. Considering these results and reviewing the literature for resistive sensors, it is feasible to control a motor through hand gesture recognition [7][8][9]. The proposed system offers an alternative solution for people with reduced mobility that, currently, is a mechanical solution with a lack of adaptability to the patient and lack of resolution.…”

Section: State Of the Artmentioning

confidence: 98%

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Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications

Vaquero-Gallardo

García

2022

JLPEA

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Electrical impedance tomography (EIT) is based on the physical principle of bioimpedance defined as the opposition that biological tissues exhibit to the flow of a rotating alternating electrical current. Consequently, here, we propose studying the characterization and classification of bioimpedance patterns based on EIT by measuring, on the forearm with eight electrodes in a non-invasive way, the potential drops resulting from the execution of six hand gestures. The starting point was the acquisition of bioimpedance patterns studied by means of principal component analysis (PCA), validated through the cross-validation technique, and classified using the k-nearest neighbor (kNN) classification algorithm. As a result, it is concluded that reduction and classification is feasible, with a sensitivity of 0.89 in the worst case, for each of the reduced bioimpedance patterns, leading to the following direct advantage: a reduction in the numbers of electrodes and electronics required. In this work, bioimpedance patterns were investigated for monitoring subjects’ mobility, where, generally, these solutions are based on a sensor system with moving parts that suffer from significant problems of wear, lack of adaptability to the patient, and lack of resolution. Whereas, the proposal implemented in this prototype, based on the so-called electrical impedance tomography, does not have these problems.

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Section: State Of the Artmentioning

confidence: 98%

“…Electrical impedance tomography (EIT) is an imaging technique utilized to study changes in the internal conductivity of the human body [1][2][3][4][5][6][7][8][9]. As a consequence, EIT can be used to infer the physiological state of the human body based on changes in the internal conductivity of the human body [3].…”

Section: Introductionmentioning

confidence: 99%

Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications

Vaquero-Gallardo

García

2022

JLPEA

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“…One of the most popular sensors for a wearable device is a resistive sensor, which has a sensing element whose resistance changes as a function of the target physical or chemical quantity. It is utilized in industrial, scientific, and commercial applications for sensing numerous physical parameters including, but not limited to, ambient temperature, humidity, pressure, strain/force, light intensity, and displacement [13][14][15]. Recent growth in the metal oxide (MOx) semiconductor sensor has enhanced the use of resistive sensors for more sophisticated applications such as chemical sensing, gas sensing, and biosensing [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

High Density Resistive Array Readout System for Wearable Electronics

Lakshminarayana

Park

et al. 2022

Sensors

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This work presents a wearable sensing system for high-density resistive array readout. The system comprising readout electronics for a high-density resistive sensor array and a rechargeable battery, was realized in a wristband. The analyzed data with the proposed system can be visualized using a custom graphical user interface (GUI) developed in a personal computer (PC) through a universal serial bus (USB) and using an Android app in smartphones via Bluetooth Low Energy (BLE), respectively. The readout electronics were implemented on a printed circuit board (PCB) and had a compact dimension of 3 cm × 3 cm. It was designed to measure the resistive sensor with a dynamic range of 1 KΩ–1 MΩ and detect a 0.1% change of the base resistance. The system operated at a 5 V supply voltage, and the overall system power consumption was 95 mW. The readout circuit employed a resistance-to-voltage (R-V) conversion topology using a 16-bit analog-to-digital converter (ADC), integrated in the Cypress Programmable System-on-Chip (PSoC®) 5LP microcontroller. The device behaves as a universal-type sensing system that can be interfaced with a wide variety of resistive sensors, including chemiresistors, piezoresistors, and thermoelectric sensors, whose resistance variations fall in the target measurement range of 1 KΩ–1 MΩ. The system performance was tested with a 60-resistor array and showed a satisfactory accuracy, with a worst-case error rate up to 2.5%. The developed sensing system shows promising results for applications in the field of the Internet of things (IoT), point-of-care testing (PoCT), and low-cost wearable devices.

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“…Gesture recognition wristband has a broad application prospect. Surface Electromyography (SEMG) sensors [3], piezoelectric sensors [4] and Force-sensing resistor (FSR) sensors [5] are suitable for gesture recognition wristband. Generally, sensors are placed around our wrist to sense tendon movements and make the prediction [6].…”

Section: Introductionmentioning

confidence: 99%

Smart Wristband for Gesture Recognition

Xia

Heidari

Ghannam

2020

2020 International Conference on UK-China Emerging Technologies (UCET)

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This paper is concerned with developing a smart gesture recognition wristband device. In this paper, tendon movements around the wrist are used to distinguish and classify different hand gestures. These tendon movements are measured by FSR sensors. Polydimethylsiloxane (PDMS) was used to encapsulate the sensors, which made the wristband elastic and comfortable for people with different wrist dimensions. The sensor data was subsequently collected and sent to a computer via low energy Bluetooth technology. MATLAB was used to process and classify the data using ensemble subspace discriminant classifier. The accuracy is about 99.4%. The paper also investigated how twisting the wrist would impact the accuracy of gesture recognition. The result illustrated that wrist rotation could make more gestures distinguishable. Overall, the wristband is wearable, chargeable, portable and capable of recognizing over 6 gestures with a high degree of accuracy.

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Wearable Resistive-based Gesture-Sensing Interface Bracelet

Cited by 6 publications

References 20 publications

Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications

Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications

High Density Resistive Array Readout System for Wearable Electronics

Smart Wristband for Gesture Recognition

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