Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

Alarcón-Aldana, Andrea Catherine; Callejas-Cuervo, Mauro; Bó, Antônio Padilha Lanari

doi:10.3390/s20215989

Cited by 63 publications

(37 citation statements)

References 56 publications

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“…Finally, further exploration is recommended in approaches related to neuromotor rehabilitation and EEG signal acquisition systems, since while some advances address the topic [ 22 , 27 , 33 , 34 , 41 , 46 ], exoskeletons with application in motor rehabilitation are more common. It is highlighted that the addition of new virtual systems can increase neuronal plasticity in a controlled rehabilitation environment, so it is necessary to consider extending the scope to systems outside the robotic exoskeletons [ 103 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Vélez-Guerrero

Callejas-Cuervo

Mazzoleni

2021

Sensors

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Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Vélez-Guerrero

Callejas-Cuervo

Mazzoleni

2021

Sensors

Self Cite

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show abstract

“…Aşkın et al [32] used VR technology based on somatosensory sensors to assist stroke patients with upper limb motor function rehabilitation. The research by Aldana et al [33] has shown that the use of video games and motion capture systems in upper limb rehabilitation can contribute to patient rehabilitation. With the rapid development of artificial intelligence and Internet technology, the research in the field of intelligent rehabilitation has increased rapidly.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Upper Limb Functionality Assessment Based on Deep Learning and Multimodal Sensing Data

et al. 2021

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Upper limb rehabilitation is an effective methodology to restore and improve the functionality of patients after multiple medical events, such as strokes, arthroscopic surgery, and breast cancer surgery. High-quality rehabilitation training can promote the independent living of patients, thus enhancing the quality of life and reducing the financial burden. Traditional training sessions have some limitations, including high expenses, low compliance, and inaccurate evaluations. This paper presents a novel approach to assist healthcare professionals in assessing the functionality of upper limbs based on multimodal sensing data and deep learning algorithms. There are five different types of sensing data employed in the proposed approach: accelerometer, angular velocity, device orientation, RGB image, and depth image data. In order to assess the accuracy of training actions, the presented approach applies two machine learning algorithms, which are the dynamic time warping-K-nearest neighbor (DTW-KNN) algorithm and the long short-term memory (LSTM) neural network. The experimental results show that multimodal sensing data can improve the modeling accuracy compared with unimodal sensing data. The LSTM model can achieve better accuracy (96.3%) than DTW-KNN (74.07%) with multimodal sensing data. Moreover, LSTM performs extremely efficiently in modeling high-dimensional sensing data.

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“…The motion capture systems used vary, depending on multiple forms of demand, for example we have Fig. 1 [11] and regarding, of course the body area treated, the distribution chart below can express the demand resultsstating this as leverage for sustaining the idea that no work of motion capture, treatment, or reproduction of motion capture and treatment technology is to be researched without demand, implying purpose. Fig.…”

Section: State Of the Artmentioning

confidence: 99%

Human Arm Motion Capture using Gyroscopic Sensors

et al. 2021

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By using the most rudimentary microcontroller chips, that receive data from sensors, and transmit the data to a computer system, thorough a virtual serial port, motion of many objects, bodies and joints can be captured. Capturing the motion and reproducing it live is not the only destination for the data usage. Recording and studying the motion data, can reduce a lot of work in a wide range of domains. Using the simplest methods to capture the data, also means making it so widely accessible for learning, editing and also developing systems that use very little processing power, granting data access for the less efficient computers. We propose using the MPU-6050 MEMS sensor in a dual instance, and the Arduino UNO microcontroller, connected to a computer for data acquisition, to capture the motion of a human arm, and reproduce it in a projected environment. Other experiments, conducted by other researchers and developers have used a higher number of sensors, and the data acquisition and recording systems were much more complex, but our research reduced the number of sensors to just two. One of the high impact innovations brought by this system, in particular, is that we’ve virtually hooked the end of one sensor to the tip of the other, creating a virtual motion chain.

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Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

Cited by 63 publications

References 56 publications

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

A Novel Approach for Upper Limb Functionality Assessment Based on Deep Learning and Multimodal Sensing Data

Human Arm Motion Capture using Gyroscopic Sensors

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