Wearable technologies for hand joints monitoring for rehabilitation: A survey

Rashid, Adnan; Hasan, Osman

doi:10.1016/j.mejo.2018.01.014

Cited by 80 publications

(67 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bending angle and level of adduction of each finger are captured by embedded sensors of different natures. As summarized by Rashid and Hasan [11], there are typically four types of sensors that can be used for hand-related tasks: bend sensors, stretch sensors, inertial measurement units (IMUs), and magnetic sensors. Most existing data gloves used for hand pose modeling are based on bend or stretch sensors, although some have a combination of multiple types of sensors.…”

Section: Wearable Devicesmentioning

confidence: 99%

“…Thus, the optimal design of a data glove may involve multiple types of sensors to joint their advantages for better performance with lower cost. A further comparison of wearable technologies on accuracy, cost, and lifetime can be found in [11].…”

Section: Evaluationsmentioning

confidence: 99%

“…Existing surveys focus either on glove-based devices [10,11] or vision-based [12][13][14] systems, since these works were carried out in two distinct research communities, i.e., human-computer interaction and computer vision. We covered both directions to provide a complete overview of the state-of-the-art for hand pose estimation, which can be particularly helpful for people making applications with hand pose estimation technology.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods

Chen

et al. 2020

Sensors

104

View full text Add to dashboard Cite

Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.

show abstract

Section: Wearable Devicesmentioning

confidence: 99%

Section: Evaluationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods

Chen

et al. 2020

Sensors

104

View full text Add to dashboard Cite

show abstract

“…Most gloves employ sensors from three categories: IMUs (inertial measurement units), bend (flex) sensors, and strain (stretch) sensors. For a complete overview we refer to the surveys [Dipietro et al 2008;Rashid and Hasan 2018]. Other work has used wrist worn IR cameras [Kim et al 2012] or magnetic sensing for hand pose estimation, and capacitive wrist bands [Truong et al 2018] or electromyography (EMG) [Saponas et al 2009] for gesture recoginition.…”

Section: Related Workmentioning

confidence: 99%

Interactive hand pose estimation using a stretch-sensing soft glove

et al. 2019

View full text Add to dashboard Cite

Fig. 1. Our stretch-sensing soft glove captures hand poses in real time and with high accuracy. It functions in diverse and challenging settings, like heavily occluded environments or changing light conditions, and lends itself to various applications. All images shown here are frames from recorded live sessions. We propose a stretch-sensing soft glove to interactively capture hand poses with high accuracy and without requiring an external optical setup. We demonstrate how our device can be fabricated and calibrated at low cost, using simple tools available in most fabrication labs. To reconstruct the pose from the capacitive sensors embedded in the glove, we propose a deep network architecture that exploits the spatial layout of the sensor itself. The network is trained only once, using an inexpensive off-the-shelf hand pose reconstruction system to gather the training data. The per-user calibration is then performed on-the-fly using only the glove. The glove's capabilities are demonstrated in a series of ablative experiments, exploring different models and calibration methods. Comparing against commercial data gloves, we achieve a 35% improvement in reconstruction accuracy.

show abstract

“…However, the high cost, adverse temperature effects and sophistication of the system make this technique not well suited for the intended application in this project. Resistive flex and stretch sensors that are extensively used in wearable devices can measure bending or flexing based on the resistance across the sensor [7,8]. Although they are simple, cheap, compact and robust, shortcomings such as nonlinearity, lower sensitivity and low accuracy restrict wider application of this technology.…”

Section: Introductionmentioning

confidence: 99%