Application of Artificial Intelligence in Rehabilitation Assessment

Mu, Pengyu; Dai, Mingyu; Ma, Xuenan

doi:10.1088/1742-6596/1802/3/032057

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…However, due to the influence of related factors, children with dental diseases do not cooperate with clinical diagnosis and treatment operations, which seriously affects the clinical diagnosis and treatment of children with dental diseases. Therefore, when performing clinical diagnosis and treatment of dental patients, limited implementation of treatment for its acute inflammation and pulpitis-related teeth, avoiding the occurrence of pain, building the confidence of the child in diagnosis and treatment, adopting surface anesthesia treatment and injection anesthesia treatment to the children are followed to ensure that the clinical diagnosis and treatment of the children can be successfully completed [ 2 ]. The data results in this article show that after comparing the total effective rate of children with dental disease in the experimental group and the reference group, the difference was statistically significant ( P < 0.05); after comparing the total satisfaction rate of parents of children with dental disease in the experimental group and the reference group, the difference was statistically significant ( P < 0.05).…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Application of a Remotely Controlled Artificial Intelligence Analgesic Pump Device in Painless Treatment of Children

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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In order to effectively improve the application of analgesic pump devices in the treatment of children, a method based on remote control artificial intelligence is proposed. 100 children with dental pulpitis who were treated in a hospital from December 2018 to December 2020 were selected as the research subjects; they were randomly divided into control group and observation group by an equidistant sampling method, with 50 cases in each group. Children in the control group were given articaine and adrenaline anesthesia, and the observation group was treated with articaine and adrenaline combined with a computer-controlled anesthesia system, the anesthesia pain degree and satisfaction degree of the two groups of children were observed and compared. The results showed that the pain score in anesthesia and intraoperative pain score in the observation group was significantly lower than that in the control group, and the differences were statistically significant ( P < 0.05 ). The total satisfaction of 96.6% patients in the observation group was significantly higher than that in the control group (84.7%) and the difference was statistically significant ( P < 0.05 ). There were no serious complications in both groups. The application of the computer anesthesia system combined with articaine adrenaline in the painless treatment of children’s dental pulp proved to have better effects, the treatment compliance is higher, and it is worthy of clinical promotion.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Application of a Remotely Controlled Artificial Intelligence Analgesic Pump Device in Painless Treatment of Children

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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“…Chip rotation or assembly/removal methods can transfer laminate circuits. Low-temperature soldering or self-adhesive electrodes using abrasion-resistant AI devices can ensure deposition and good adhesion between different surfaces [6] .…”

Section: Manufacturing Technology For Flexible Wearable Devicesmentioning

confidence: 99%

Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: With the rapid development of artificial intelligence technology, wearable artificial intelligence devices show great potential in medical rehabilitation. This study explores the Real Time monitoring effect of AI wearable devices in the rehabilitation process of track and field athletes. The application of this technology in rehabilitation monitoring was investigated through the introduction of advanced sensing technology and data analysis algorithms to provide track and field athletes with more scientific and personalized rehabilitation programs. OBJECTIVES: A group of track and field athletes was selected as the research object and equipped with an artificial intelligence wearable device, which is capable of Real Time monitoring of the athletes' physiological parameters, sports postures, joint mobility, and other rehabilitation-related data. An individualized rehabilitation model was established through the data collected by these sensors, and advanced artificial intelligence algorithms were used to analyze the data in Real Time. At the same time, the sensor data were combined with the actual performance of the athletes' rehabilitation training to comprehensively assess the effectiveness of AI wearable devices in rehabilitation monitoring. METHODS: This study aims to assess the effect of Real Time monitoring of AI wearable devices in the rehabilitation of track and field athletes and to explore their potential application in the rehabilitation process. Real Time tracking of athletes' physiological status and athletic performance aims to provide more accurate and timely information to rehabilitation doctors and coaches to optimize the rehabilitation training program and promote the rehabilitation process of athletes. RESULTS: The study showed that artificial intelligence wearable devices have significant Real Time monitoring effects in rehabilitating track and field athletes. Through Real Time monitoring of data such as physiological parameters, sports posture, and joint mobility, the rehabilitation team was able to identify potential problems and adjust the rehabilitation program in a more timely manner. Athletes using artificial intelligence wearable devices improved the personalization and targeting of rehabilitation training, and the rehabilitation effect was significantly better than that of traditional monitoring methods. CONCLUSION: This study concludes that artificial intelligence wearable devices perform well in rehabilitating track and field athletes, providing a more scientific and comprehensive means of rehabilitation monitoring. Through Real Time tracking, the rehabilitation team could better understand the rehabilitation progress of the athletes, adjust the rehabilitation program in a targeted manner, and improve the rehabilitation effect. However, future research still needs to optimize the performance of the devices further, expand the sample size, and thoroughly study the monitoring needs at different stages of rehabilitation to better meet the individualized requirements of track and field athletes' rehabilitation process.

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“…In recent years, the field of robot-aided rehabilitation has attracted considerable attention, resulting in a proliferation of scholarly works. The existing body of literature encompasses several comprehensive reviews delving into the application of exoskeletons in lower-limb rehabilitation (Prakash et al, 2018;Khera and Kumar, 2020;Mu et al, 2021;Harris et al, 2022). However, this review distinguishes itself by uniquely focusing on the integration of advanced Artificial Intelligence (AI) methodologies within this domain, presenting a novel and innovative contribution.…”

Section: Current Reviewmentioning

confidence: 99%

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

Coser,

Tamantini,

Soda

et al. 2024

Front. Robot. AI

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Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.

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Application of Artificial Intelligence in Rehabilitation Assessment

Cited by 7 publications

References 3 publications

[Retracted] Application of a Remotely Controlled Artificial Intelligence Analgesic Pump Device in Painless Treatment of Children

[Retracted] Application of a Remotely Controlled Artificial Intelligence Analgesic Pump Device in Painless Treatment of Children

Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

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