A Wireless Multi-Channel Capacitive Sensor System for Efficient Glove-Based Gesture Recognition With AI at the Edge

Pan, Jieming; Luo, Yuxuan; Li, Yida; Tham, Chen-Khong; Heng, Chun-Huat; Thean, Aaron

doi:10.1109/tcsii.2020.3010318

Cited by 45 publications

(27 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gestures are a common way for people to communicate and convey information in daily life. A lot of research has been done on gesture recognition based on finger bending information [ 5 , 19 , 20 , 21 , 22 ]. In this paper, static gesture recognition based on the designed data glove and neural network is conducted to verify the effectiveness of the designed glove in daily application.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Fahn et al developed a data glove by using five magnetic coils placed on the palmar surface to measure 10 degrees of freedom of a hand [ 18 ]. Pan et al presented a glove with 16 capacitive sensors embedded to capture the hand gesture [ 19 ]. In 10 American Sign Language gesture recognition experiments, they got a classification accuracy of 99.7% by using machine learning algorithms and directly processing the code-modulated signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

Wang

Cao

et al. 2021

Micromachines

View full text Add to dashboard Cite

Capturing finger joint angle information has important applications in human–computer interaction and hand function evaluation. In this paper, a novel wearable data glove is proposed for capturing finger joint angles. A sensing unit based on a grating strip and an optical detector is specially designed for finger joint angle measurement. To measure the angles of finger joints, 14 sensing units are arranged on the back of the glove. There is a sensing unit on the back of each of the middle phalange, proximal phalange, and metacarpal of each finger, except for the thumb. For the thumb, two sensing units are distributed on the back of the proximal phalange and metacarpal, respectively. Sensing unit response tests and calibration experiments are conducted to evaluate the feasibility of using the designed sensing unit for finger joint measurement. Experimental results of calibration show that the comprehensive precision of measuring the joint angle of a wooden finger model is 1.67%. Grasping tests and static digital gesture recognition experiments are conducted to evaluate the performance of the designed glove. We achieve a recognition accuracy of 99% by using the designed glove and a generalized regression neural network (GRNN). These preliminary experimental results indicate that the designed data glove is effective in capturing finger joint angles.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

Wang

Cao

et al. 2021

Micromachines

View full text Add to dashboard Cite

show abstract

“…Chen et al [2] presented a wearable hand rehabilitation system that offers 16 kinds of finger gestures with an accuracy of 93.32%. Pan et al [3] presented a wireless smart glove that can recognize 10 American Sign Language gestures, and the highest testing classification accuracy of our system is 99.7%. Maitre et al [4] developed a data glove prototype allowing for the recognition of objects in 8 basic daily activities with an accuracy of 95%.…”

Section: Introductionmentioning

confidence: 87%

A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements

Zheng

2021

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.

show abstract

“…Emerging technologies on wearable systems encompassing materials, circuits, and artificial intelligence give rise to truly intelligent human–machine interaction (HMI) systems. − These systems are the key enablers for direct communication between humans and machines to perform complex tasks, which pushes the boundary of human capability. − With our excellent finger dexterity and versatile gesticulation capability, humans manipulate objects and communicate meaning through our hands effortlessly, and thus, a glove is a naturally appropriate archetype to capture such information. − In consideration of extended operation in the field, a wearable device requires to be lightweight, highly flexible, conformal, and with low power consumption . Although several pioneering efforts have been reported to achieve some of these desirable traits, substantial challenges remain to be addressed.…”

mentioning

confidence: 99%

“…Although such a configuration can achieve an extreme readout speed on sensor input, it consumes huge power and hardware resources with massive raw data transmission, making it unsuitable for wearable applications. In our implementation, we customize a specific interface circuit to drive the frontend sensor array by integrating a CDMA multiplexing technology on direct capacitance to digital conversion (CDC). ,− The analog multichannel capacitor inputs are modulated with orthogonal codes and further converted into digital format via a single-channel ADC as shown in Figure S5. This interface circuit is designed in-house and fabricated using a common 130 nm complementary metal-oxide-semiconductor (CMOS) process from GlobalFoundries (GF) with a silicon area of 1.35 mm 2 , and it operates at a sampling frequency of 2 kHz (0.5 ms per channel) while only consuming 8.2 μW energy, which is the lowest power consumption compared to other multichannel capacitive sensor interfaces reported (Figure S5).…”

mentioning

confidence: 99%

Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition

Pan

Luo

et al. 2021

ACS Sens.

Self Cite

View full text Add to dashboard Cite

As 5G communication technology allows for speedier access to extended information and knowledge, a more sophisticated human−machine interface beyond touchscreens and keyboards is necessary to improve the communication bandwidth and overcome the interfacing barrier. However, the full extent of human interaction beyond operation dexterity, spatial awareness, sensory feedback, and collaborative capability to be replicated completely remains a challenge. Here, we demonstrate a hybridflexible wearable system, consisting of simple bimodal capacitive sensors and a customized low power interface circuit integrated with machine learning algorithms, to accurately recognize complex gestures. The 16 channel sensor array extracts spatial and temporal information of the finger movement (deformation) and hand location (proximity) simultaneously. Using machine learning, over 99 and 91% accuracy are achieved for user-independent static and dynamic gesture recognition, respectively. Our approach proves that an extremely simple bimodal sensing platform that identifies local interactions and perceives spatial context concurrently, is crucial in the field of sign communication, remote robotics, and smart manufacturing.

show abstract

A Wireless Multi-Channel Capacitive Sensor System for Efficient Glove-Based Gesture Recognition With AI at the Edge

Cited by 45 publications

References 8 publications

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

Development of a Low-Cost Wearable Data Glove for Capturing Finger Joint Angles

A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements

Hybrid-Flexible Bimodal Sensing Wearable Glove System for Complex Hand Gesture Recognition

Contact Info

Product

Resources

About