A Novel Gesture Recognition System for Intelligent Interaction with a Nursing-Care Assistant Robot

Yang, Geng; Lv, Honghao; Chen, Feiyu; Pang, Zhibo; Wang, Jin; Yang, Huayong; Zhang, Junhui

doi:10.3390/app8122349

Cited by 22 publications

(15 citation statements)

References 38 publications

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“…In [28], a camera is attached to the wrist, and the hand motion is recognized using the captured video. In addition, if we note that the direction of measurement of the imaging sensor reflects the eyesight of the prosthetic hands, the conventional technology of the gaze input interface [29] can also be applied.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Assistance for Myoelectric Hand Control

Kubozono

Fukuda

et al. 2020

IEEE Access

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Conventional control systems for prosthetic hands use myoelectric signals as an interface, but it is impossible to realize complex and flexible human hand movements with only myoelectric signals. A promising control scheme for prosthetic hands uses computer vision to assist in grasping objects. It features an imaging sensor, and the control system is capable of recognizing an object placed in the environment. Then, a gripping pattern can be selected from some predefined candidates according the recognized object. However, previous studies assumed that only one object exists in the environment. If there are multiple target objects in the environment, the hand could become confused in attempting to find the target object. This study addresses this problem and proposes a method to determine the target object from multiple objects. The proposed method is able to determine the target object by estimating the positional relationship between the artificial hand and the objects, as well as the motion of the hand. To verify the validity and effectiveness, we implemented the proposed method in a vision-based prosthetic hands control system and conducted pick-and-place experiments. The experiments confirm that the proposed method can accurately estimate the target object in accordance with the user's intention.

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Section: Related Workmentioning

confidence: 99%

Vision-Based Assistance for Myoelectric Hand Control

Kubozono

Fukuda

et al. 2020

IEEE Access

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“…Gesture control provides intuitive and efficient solutions in robotics, with specific applications including unmanned ground vehicles for military surveillance [18] and improved manufacturing safety and efficiency [19], robotic arm manipulation for prosthetic devices [20], robot-assisted surgeries [21], and robotic nursing-care assistants [22]. Rehabilitation robots that facilitate therapy with quantitative measures of movement performance have demonstrated potential as complementary assessment tools; however, they are typically limited to a clinical setting and are not easily translated to the home environment [23].…”

Section: Introductionmentioning

confidence: 99%

A Gesture-Controlled Rehabilitation Robot to Improve Engagement and Quantify Movement Performance

Segal

Lesak

Silverman

et al. 2020

Sensors

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Rehabilitation requires repetitive and coordinated movements for effective treatment, which are contingent on patient compliance and motivation. However, the monotony, intensity, and expense of most therapy routines do not promote engagement. Gesture-controlled rehabilitation has the potential to quantify performance and provide engaging, cost-effective treatment, leading to better compliance and mobility. We present the design and testing of a gesture-controlled rehabilitation robot (GC-Rebot) to assess its potential for monitoring user performance and providing entertainment while conducting physical therapy. Healthy participants (n = 11) completed a maze with GC-Rebot for six trials. User performance was evaluated through quantitative metrics of movement quality and quantity, and participants rated the system usability with a validated survey. For participants with self-reported video-game experience (n = 10), wrist active range of motion across trials (mean ± standard deviation) was 41.6 ± 13° and 76.8 ± 16° for pitch and roll, respectively. In the course of conducting a single trial with a time duration of 68.3 ± 19 s, these participants performed 27 ± 8 full wrist motion repetitions (i.e., flexion/extension), with a dose-rate of 24.2 ± 5 reps/min. These participants also rated system usability as excellent (score: 86.3 ± 12). Gesture-controlled therapy using the GC-Rebot demonstrated the potential to be an evidence-based rehabilitation tool based on excellent user ratings and the ability to monitor at-home compliance and performance.

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“…The human hand is a complex mechanical device with 19 bones, 14 joints and over 25 degrees of freedom [23]. Due to this mechanical complexity, most of the studies done in the field of rehabilitation robotics have focused on regaining upper-limb mobility [24][25][26][27][28][29] with less focus on robotic rehabilitation techniques of the hand and fingers [23]. The construction and actuation methods used in current robotic exoskeleton technologies generally result in expensive, bulky and physically confining devices.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand–Motor Function

et al. 2019

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We present the design and validation of a low-cost, customizable and 3D-printed anthropomorphic soft robotic hand exoskeleton for rehabilitation of hand injuries using remotely administered physical therapy regimens. The design builds upon previous work done on cable actuated exoskeleton designs by implementing the same kinematic functionality, but with the focus shifted to ease of assembly and cost effectiveness as to allow patients and physicians to manufacture and assemble the hardware necessary to implement treatment. The exoskeleton was constructed solely from 3D-printed and widely available off-the-shelf components. Control of the actuators was realized using an Arduino microcontroller, with a custom-designed shield to facilitate ease of wiring. Tests were conducted to verify that the range of motion of the digits and the forces exerted at the fingertip coincided with those of a healthy human hand.

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A Novel Gesture Recognition System for Intelligent Interaction with a Nursing-Care Assistant Robot

Cited by 22 publications

References 38 publications

Vision-Based Assistance for Myoelectric Hand Control

Vision-Based Assistance for Myoelectric Hand Control

A Gesture-Controlled Rehabilitation Robot to Improve Engagement and Quantify Movement Performance

A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand–Motor Function

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