Non-invasive optical detection of hand gestures

Ortega-Avila, Santiago; Rakova, Bogdana; Sadi, Sajid; Mistry, Pranav

doi:10.1145/2735711.2735801

Cited by 19 publications

(3 citation statements)

References 8 publications

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“…Near-infrared spectroscopy (NIRS) is a commonly used chemical component analysis method for ambulatory monitoring of tissue oxygenation and haemodynamics [143]. Hand gesture changes lead to vascular deformation and cause hemodynamics variations, and thus, they can be captured by the Near-infrared sensor and recognized by NIRS analysis [112]. A time-of-flight (ToF) sensor is another commonly used optical sensor to measure distance, and it has previously used to measure the depth information of an image.…”

Section: Conventional Machine Learningmentioning

confidence: 99%

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Jiang

Kang

Song

et al. 2022

IEEE Rev. Biomed. Eng.

129

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Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review was to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

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Section: Conventional Machine Learningmentioning

confidence: 99%

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Jiang

Kang

Song

et al. 2022

IEEE Rev. Biomed. Eng.

129

View full text Add to dashboard Cite

show abstract

“…Dementyev and Paradiso [ 41 ], Fukui et al [ 42 ], McIntosh et al [ 43 ], and Rekimoto [ 44 ] use a wristband to detect finger movements by measuring changes in the arm contour caused by the displacement of the bones and chords during the movement. Zhang and Harrison [ 45 ] measure these variations in the arrangement of chords and bones using tomography, McIntosh et al [ 46 ] and Ortega-Avila et al [ 47 ] by infrared lights, and McIntosh et al [ 48 ] by ultrasound imaging. Moreover, electromyography measuring the electrical activity produced by muscles in the forearm can be used to detect and distinguish finger movements as shown by Amma et al [ 49 ], Haque et al [ 50 ], and Huang et al [ 51 ].…”

Section: Related Workmentioning

confidence: 99%

PeriSense: Ring-Based Multi-Finger Gesture Interaction Utilizing Capacitive Proximity Sensing

Wilhelm

Krakowczyk

Albayrak

2020

Sensors

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Rings are widely accepted wearables for gesture interaction. However, most rings can sense only the motion of one finger or the whole hand. We present PeriSense, a ring-shaped interaction device enabling multi-finger gesture interaction. Gestures of the finger wearing ring and its adjacent fingers are sensed by measuring capacitive proximity between electrodes and human skin. Our main contribution is the determination of PeriSense’s interaction space involving the evaluation of capabilities and limitations. We introduce a prototype named PeriSense, analyze the sensor resolution at different distances, and evaluate finger gestures and unistroke gestures based on gesture sets allowing the determination of the strengths and limitations. We show that PeriSense is able to sense the change of conductive objects reliably up to 2.5 cm. Furthermore, we show that this capability enables different interaction techniques such as multi-finger gesture recognition or two-handed unistroke input.

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“…Sensor-based methods rely on wearable devices to capture motion data and recognize hand gestures. These devices include sensors such as accelerometers [ 10 ], inertial measurement units (IMUs) [ 11 ], optical sensors [ 12 ], and surface electromyography (sEMG) devices [ 13 ]. This approach has demonstrated high accuracy in recognizing dynamic hand gestures.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Monocular Skeleton-Based Hand Gesture Recognition Using 3D-Jointsformer

Zhong,

del-Blanco,

Berjón

et al. 2023

Sensors

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Automatic hand gesture recognition in video sequences has widespread applications, ranging from home automation to sign language interpretation and clinical operations. The primary challenge lies in achieving real-time recognition while managing temporal dependencies that can impact performance. Existing methods employ 3D convolutional or Transformer-based architectures with hand skeleton estimation, but both have limitations. To address these challenges, a hybrid approach that combines 3D Convolutional Neural Networks (3D-CNNs) and Transformers is proposed. The method involves using a 3D-CNN to compute high-level semantic skeleton embeddings, capturing local spatial and temporal characteristics of hand gestures. A Transformer network with a self-attention mechanism is then employed to efficiently capture long-range temporal dependencies in the skeleton sequence. Evaluation of the Briareo and Multimodal Hand Gesture datasets resulted in accuracy scores of 95.49% and 97.25%, respectively. Notably, this approach achieves real-time performance using a standard CPU, distinguishing it from methods that require specialized GPUs. The hybrid approach’s real-time efficiency and high accuracy demonstrate its superiority over existing state-of-the-art methods. In summary, the hybrid 3D-CNN and Transformer approach effectively addresses real-time recognition challenges and efficient handling of temporal dependencies, outperforming existing methods in both accuracy and speed.

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Non-invasive optical detection of hand gestures

Cited by 19 publications

References 8 publications

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

PeriSense: Ring-Based Multi-Finger Gesture Interaction Utilizing Capacitive Proximity Sensing

Real-Time Monocular Skeleton-Based Hand Gesture Recognition Using 3D-Jointsformer

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