1
|
Pancholi S, Wachs JP, Duerstock BS. Use of Artificial Intelligence Techniques to Assist Individuals with Physical Disabilities. Annu Rev Biomed Eng 2024; 26:1-24. [PMID: 37832939 DOI: 10.1146/annurev-bioeng-082222-012531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
Assistive technologies (AT) enable people with disabilities to perform activities of daily living more independently, have greater access to community and healthcare services, and be more productive performing educational and/or employment tasks. Integrating artificial intelligence (AI) with various agents, including electronics, robotics, and software, has revolutionized AT, resulting in groundbreaking technologies such as mind-controlled exoskeletons, bionic limbs, intelligent wheelchairs, and smart home assistants. This article provides a review of various AI techniques that have helped those with physical disabilities, including brain-computer interfaces, computer vision, natural language processing, and human-computer interaction. The current challenges and future directions for AI-powered advanced technologies are also addressed.
Collapse
Affiliation(s)
- Sidharth Pancholi
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Juan P Wachs
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Bradley S Duerstock
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
- School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA
| |
Collapse
|
2
|
Zhang S, Yu N, Guo Z, Huo W, Han J. Single-Channel sEMG-Based Estimation of Knee Joint Angle Using a Decomposition Algorithm With a State-Space Model. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4703-4712. [PMID: 38015663 DOI: 10.1109/tnsre.2023.3336317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Accurate human motion estimation is crucial for effective and safe human-robot interaction when using robotic devices for rehabilitation or performance enhancement. Although surface electromyography (sEMG) signals have been widely used to estimate human movements, conventional sEMG-based methods, which need sEMG signals measured from multiple relevant muscles, are usually subject to some limitations, including interference between sEMG sensors and wearable robots/environment, complicated calibration, as well as discomfort during long-term routine use. Few methods have been proposed to deal with these limitations by using single-channel sEMG (i.e., reducing the sEMG sensors as much as possible). The main challenge for developing single-channel sEMG-based estimation methods is that high estimation accuracy is difficult to be guaranteed. To address this problem, we proposed an sEMG-driven state-space model combined with an sEMG decomposition algorithm to improve the accuracy of knee joint movement estimation based on single-channel sEMG signals measured from gastrocnemius. The effectiveness of the method was evaluated via both single- and multi-speed walking experiments with seven and four healthy subjects, respectively. The results showed that the normal root-mean-squared error of the estimated knee joint angle using the method could be limited to 15%. Moreover, this method is robust with respect to variations in walking speeds. The estimation performance of this method was basically comparable to that of state-of-the-art studies using multi-channel sEMG.
Collapse
|
3
|
Wang X, Xu M. Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis. PLoS One 2023; 18:e0285015. [PMID: 37363923 DOI: 10.1371/journal.pone.0285015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/13/2023] [Indexed: 06/28/2023] Open
Abstract
OBJECTIVE This work was aimed to analyze the effect of vitamin energy drink on muscle fatigue by surface electromyography (SEMG) and ultrasonic bioimaging (USBI). METHODS 20 healthy men were selected to do increasing load fatigue test. Surface electromyographic signals and ultrasonic biological images were collected based on wavelet threshold function with improved thresholds. Time domain and frequency domain characteristic integrated electromyography (IEMG), root mean square amplitude (RMS), average power frequency (MPF), and surface and deep muscle morphological changes were analyzed. Hemoglobin concentration (HB), red blood cell number (RBC), mean volume of red blood cell (MCV), blood lactic acid (BLA), malondialdehyde (MDA), and phosphocreatine kinase (CK) were measured. RESULTS 1) the Accuracy (94.10%), Sensitivity (94.43%), Specificity (93.75%), and Precision (94.07%) of the long and short-term memory (LSTM) specificity for muscle fatigue recognition were higher than those of other models. 2) Compared with the control group, the levels of BLA, MDA, and CK in the experimental group were decreased and HB levels were increased after exercise (P < 0.05). 3) IEMG and RMS of the experimental group were higher than those of the control group, and increased with time (P < 0.05). 4) The mean amplitude of the response signal decreased with time. Compared with the control group, the surface muscle thickness, deep muscle thickness, total muscle thickness, contrast, and homogeneity (HOM) decreased in the experimental group; while the angular second moment (ASM) and contrast increased, showing great differences (P < 0.05). CONCLUSION Surface electromyographic signal and ultrasonic biological image can be used as auxiliary monitoring techniques for muscle fatigue during exercise. Drinking vitamin energy drinks before exercise can relieve physical fatigue to a certain extent and promote the maintenance of muscle microstructure.
Collapse
Affiliation(s)
- Xindi Wang
- School of Aerospace, Harbin Institute of Technology, Harbin, Heilongjiang, China
- China Basketball College, Beijing Sport University, Beijing, Beijing, China
| | - Mengtao Xu
- China Basketball College, Beijing Sport University, Beijing, Beijing, China
| |
Collapse
|
4
|
Fu J, Wang H, Na R, Jisaihan A, Wang Z, Ohno Y. Recent advancements in digital health management using multi-modal signal monitoring. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:5194-5222. [PMID: 36896542 DOI: 10.3934/mbe.2023241] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.
Collapse
Affiliation(s)
- Jiayu Fu
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
| | - Haiyan Wang
- Ma'anshan University, maanshan 243000, China
| | - Risu Na
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
- Shanghai Jian Qiao University, Shanghai 201315, China
| | - A Jisaihan
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
| | - Zhixiong Wang
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
- Ma'anshan University, maanshan 243000, China
| | - Yuko Ohno
- Department of Mathematical Health Science, Graduate School of Medicine, Osaka University, Osaka 5650871, Japan
| |
Collapse
|
5
|
Zhang S, Lu J, Huo W, Yu N, Han J. Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network. Front Neurorobot 2022; 16:978014. [PMID: 36386394 PMCID: PMC9640579 DOI: 10.3389/fnbot.2022.978014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 09/28/2022] [Indexed: 11/24/2022] Open
Abstract
Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this study, a feature-guided convolutional neural network (FG-CNN) has been proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features have been fused into a CNN model to guide CNN feature extraction, and both handcrafted and CNN-extracted features were applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects were carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), were separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrated that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and the estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, comparative studies showed that the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots.
Collapse
Affiliation(s)
- Song Zhang
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
| | - Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
| | - Weiguang Huo
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
- *Correspondence: Ningbo Yu
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, China
- Weiguang Huo
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, China
| |
Collapse
|