Robot Assisted Physiotherapy to Support Rehabilitation of Facial Paralysis

Jayatilake, Dushyantha; Isezaki, Takashi; Teramoto, Yohei; Eguchi, Kiyoshi; Suzuki, Kenji

doi:10.1109/tnsre.2013.2279169

Cited by 23 publications

(16 citation statements)

References 20 publications

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“…For normal human is not a problem to recognize various expressions in face (without delay), however recognition expression of face by computer in real time is still a big problem [32], [28]. The past decade has witnessed many new developments in facial expression analysis due to its wide application in robotic vision [37], [24], forensics [5], affective computing, man-machine interaction [29], [18] and even medical diagnosis [24]. Therefore we can say that the correct classification of emotional state is an important role in various applications and industries of our life, for example: robust tools for behavioral people research, speech processing and speech recognition or access control of people by building monitoring, etc.…”

Section: Introductionmentioning

confidence: 99%

Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System

Magdin¹,

Prikler²

2019

IJIMAI

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At the present time, various freely available or commercial solutions are used to classify the subject's emotional state. Classification of the emotional state helps us to understand how the subject feels and what he is experiencing in a particular situation. Classification of the emotional state can thus be used in various areas of our life from neuromarketing, through the automotive industry (determining how emotions affect driving), to implementing such a system into the learning process. The learning process, which is the (mutual) interaction between the teacher and the learner, is an interesting area in which individual emotional states can be explored. In this pedagogical-psychological area several research studies were realized. These studies in some cases demonstrated the important impact of the emotional state on the results of the students. However, for comparison and unambiguous classification of the emotional state most of these studies used the instructed (even constructed) stereotypical facial expressions of the most well-known test databases (Jaffe is a typical example). Such facial expressions are highly standardized, and the software can recognize them with a fairly big percentage, but this does not necessarily point to the actual success rate of the subject's emotional classification in such a test because the similarity to real emotional expression remains unknown. Therefore, we examined facial expressions in real situations. We have subsequently compared these examined facial expressions with the instructed expressions of the same emotions (the Jaffe database). The overall average classification score in real facial expressions was 94.58%.

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Section: Introductionmentioning

confidence: 99%

Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System

Magdin¹,

Prikler²

2019

IJIMAI

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“…The robot suit HAL (CYBERDYNE Inc.) is one of the pioneering works, which can support human gait function based on the wearer's intention using surface myoelectronic potentials [2]. In the current state, wearable robots mainly support articulation of body joints, but only a few technologies support other body movements such as facial movement [3]. It is important that the robots can support body movement other than joint articulation to further advance the field of wearable robotics.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Xin et al [8] proposed a functional-electrical stimulation method to restore an eye blink based on myoelectric signals of the orbicularis oculi on the healthy side. We also proposed a wearable robot to support movement of the mouth corner in order to reconstruct facial expressions [3]. Based on detection of myoelectric signals which mimic muscle on the healthy side, this robot provides non-invasive physical support by pulling the facial skin.…”

Section: Introductionmentioning

confidence: 99%

A wearable soft robot for movement assistance on eyelid closure

2018

Self Cite

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We present a facial wearable robot to support eyelid movement for patients with hemifacial paralysis. People with facial paralysis are not able to blink, which leads to dry eyes and could cause permanent damage to the cornea. To address this issue, we developed a robotic system to support eyelid movement on the paralyzed side based on voluntary eyelid movement on the healthy side. The robot has a novel mechanism for supporting eyelid movement and is made from soft material, which we call the eyelid gating mechanism (ELGM). We defined the requirements for eyelid manipulation. Then, we introduced design rules for the ELGM based on this definition, and conducted a deformation analysis. As a result, we could control deformation so that it is tailored to eyelid movement with simple rotational input at two hinged ends. This system provides appropriate support for eyelid movement in a non-invasive and gentle manner, based on voluntary eyelid movement on the healthy side. In this paper, we present a performance evaluation of the developed ELGM and the wearable facial robot. We confirm that ELGM could aid eyelid closure in 8 out of 10 patients. Further, we find that the time lag between eyelid closure detection and completion was 236.9 ± 19.4 ms. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…HE past decade has witnessed many new developments in facial expression analysis due to its wide application in robotic vision [1,2], forensics [3], affective computing [4], man-machine interaction [4,5], and even medical diagnosis [2]. Especially, recent advances in the paradigm of Internet-of-Things (IoT) has made it available to take face photos anywhere from mobile devices, bringing out a wide needs of effective facial expression analysis over IoT devices.…”

Section: Introductionmentioning

confidence: 99%

“…Especially, recent advances in the paradigm of Internet-of-Things (IoT) has made it available to take face photos anywhere from mobile devices, bringing out a wide needs of effective facial expression analysis over IoT devices. For example, a patient at home can send their facial images to their GP's mobile device for an automated diagnosis of their facial muscles [2] from their facial expression analysis; a passenger back to home can send a photo of their facial expression images from their mobile to their intelligent home to interact with a smart home-care system [5]. With these potential applications, facial analysis over mobile devices has become an emerging need of this function.…”

Section: Introductionmentioning

confidence: 99%

Emotion recognition from scrambled facial images via many graph embedding

Jiang

Cheheb

et al. 2017

Pattern Recognition

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Facial expression verification has been extensively exploited due to its wide application in affective computing, robotic vision, man-machine interaction and medical diagnosis. With the recent development of Internet-of-Things (IoT), there is a need of mobile-targeted facial expression verification, where face scrambling has been proposed for privacy protection during image/video distribution over public network. Consequently, facial expression verification needs to be carried out in a scrambled domain, bringing out new challenges in facial expression recognition. An immediate impact from face scrambling is that conventional semantic facial components become not identifiable, and 3D face models cannot be clearly fitted to a scrambled image. Hence, the classical facial action coding system cannot be applied to facial expression recognition in the scrambled domain. To cope with chaotic signals from face scrambling, this paper proposes an new approach – Many Graph Embedding (MGE) to discover discriminative patterns from the subspaces of chaotic patterns, where the facial expression recognition is carried out as a fuzzy combination from many graph embedding. In our experiments, the proposed MGE was evaluated on three scrambled facial expression datasets: JAFFE, MUG and CK++. The benchmark results demonstrated that the proposed method is able to improve the recognition accuracy, making our method a promising candidate for the scrambled facial expression recognition in the emerging privacy-protected IoT applications

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Robot Assisted Physiotherapy to Support Rehabilitation of Facial Paralysis

Cited by 23 publications

References 20 publications

Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System

Are Instructed Emotional States Suitable for Classification? Demonstration of How They Can Significantly Influence the Classification Result in An Automated Recognition System

A wearable soft robot for movement assistance on eyelid closure

Emotion recognition from scrambled facial images via many graph embedding

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