VRheab: a fully immersive motor rehabilitation system based on recurrent neural network

Avola, Danilo; Cinque, Luigi; Foresti, Gian Luca; Marini, Marco Raoul; Pannone, Daniele

doi:10.1007/s11042-018-5730-1

Cited by 38 publications

(32 citation statements)

References 54 publications

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“…Correct evaluations by the algorithms reported above can provide countless advantages in terms of maintenance and hazard determination. Even in hand and body rehabilitation, as shown in References [ 4 , 5 , 6 ], last few years have seen the proliferation of vision-based systems able to provide measurements and predictions about the recovery degree of lost skills by patients affected with strokes and degenerative diseases. The first work focused on a hand skeleton model together with pose estimation and tracking algorithms both to determine palm and fingers pose estimation and to track their movements during a rehabilitation exercise.…”

Section: Introductionmentioning

confidence: 99%

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Avola

Cinque

Fagioli

et al. 2020

Sensors

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Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

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Section: Introductionmentioning

confidence: 99%

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Avola

Cinque

Fagioli

et al. 2020

Sensors

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“…Among the various types of rehabilitation, neurocognitive rehabilitation seems to be the one that could benefit most from the use of virtual environments with a high level of interaction [28][29][30]. For this reason, and on the basis of our previous experiences in hand and body motor rehabilitation based on virtual environments [31][32][33][34], in this paper a tool for immersive neurocognitive rehabilitation is presented. Notice that, even if the tool proposed in this paper inherits technical and theoretical experiences from the just cited systems, with respect to them, and more importantly, with respect to the current state-of-the-art, it presents totally re-designed and re-developed architecture and modules.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…Similarly to the works in [33,34], the proposed tool is based on the immersive virtual reality. But, unlike all our previous works [31][32][33][34], it is specifically designed for neurocognitive rehabilitation, thus providing a new set of serious games based on a different paradigm; • Unlike the current state-of-the-art, the tool allows us to create 3D environments both automatically (i.e., by using a planimetry) and manually (i.e., created ad-hoc by a therapist). In this way, it is possible to recreate an environment similar to the patient's home, thus decreasing the stress induced by the rehabilitation sessions; • Another novelty with respect to the common literature is that the tool allows us to create serious games with an increasing level of difficulty, thus allowing both patients to familiarize with the exercises and therapists to adapt game parameters according to the patients' status; • A final not common novelty of the proposed tool is the implementation of a random mechanism, inside each serious game, that allows it, for example, to place the objects in the rooms always in different positions, thus increasing the longevity of the rehabilitative exercises and, at the same time, limiting the habituation factor of the patients that, as well known, is a crucial aspect of the neurocognitive rehabilitation.…”

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confidence: 99%

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Design of a 3D Platform for Immersive Neurocognitive Rehabilitation

2020

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In recent years, advancements in human–computer interaction (HCI) have enabled the development of versatile immersive devices, including Head-Mounted Displays (HMDs). These devices are usually used for entertainment activities as video-gaming or augmented/virtual reality applications for tourist or learning purposes. Actually, HMDs, together with the design of ad-hoc exercises, can also be used to support rehabilitation tasks, including neurocognitive rehabilitation due to strokes, traumatic brain injuries, or brain surgeries. In this paper, a tool for immersive neurocognitive rehabilitation is presented. The tool allows therapists to create and set 3D rooms to simulate home environments in which patients can perform tasks of their everyday life (e.g., find a key, set a table, do numerical exercises). The tool allows therapists to implement the different exercises on the basis of a random mechanism by which different parameters (e.g., objects position, task complexity) can change over time, thus stimulating the problem-solving skills of patients. The latter aspect plays a key role in neurocognitive rehabilitation. Experiments obtained on 35 real patients and comparative evaluations, conducted by five therapists, of the proposed tool with respect to the traditional neurocognitive rehabilitation methods highlight remarkable results in terms of motivation, acceptance, and usability as well as recovery of lost skills.

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“…The proposed applications of gesture recognition include intelligent wheelchairs [5], interactive presentation systems [6], automatic user state recognition for television control systems [7], and robot-assisted living [8]. There are two main types of hand gesture recognition: vision-based [9][10][11][12] and inertial sensor-based [13][14][15]. The former is used for 3D recognition in large systems such as image processing and the latter is commonly used to estimate real-time gait cycles and recognize hand gestures [16].…”

Section: Introductionmentioning

confidence: 99%

A Hand Gesture Recognition Method using Inertial Sensor for Rapid Operation on Embedded Device

2020

KSII TIIS

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We propose a hand gesture recognition method that is compatible with a head-up display (HUD) including small processing resource. For fast link adaptation with HUD, it is necessary to rapidly process gesture recognition and send the minimum amount of driver hand gesture data from the wearable device. Therefore, we use a method that recognizes each hand gesture with an inertial measurement unit (IMU) sensor based on revised correlation matching. The method of gesture recognition is executed by calculating the correlation between every axis of the acquired data set. By classifying pre-defined gesture values and actions, the proposed method enables rapid recognition. Furthermore, we evaluate the performance of the algorithm, which can be implanted within wearable bands, requiring a minimal process load. The experimental results evaluated the feasibility and effectiveness of our decomposed correlation matching method. Furthermore, we tested the proposed algorithm to confirm the effectiveness of the system using pre-defined gestures of specific motions with a wearable platform device. The experimental results validated the feasibility and effectiveness of the proposed hand gesture recognition system. Despite being based on a very simple concept, the proposed algorithm showed good performance in recognition accuracy

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VRheab: a fully immersive motor rehabilitation system based on recurrent neural network

Cited by 38 publications

References 54 publications

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Design of a 3D Platform for Immersive Neurocognitive Rehabilitation

A Hand Gesture Recognition Method using Inertial Sensor for Rapid Operation on Embedded Device

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