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Lee IJ, Hu YH, Hsiao PC, Yang SY, Lin HT, Chen YC, Lin BS. AI-Based Automatic System for Assessing Upper-Limb Spasticity of Patients With Stroke Through Voluntary Movement. IEEE J Biomed Health Inform 2024; 28:742-752. [PMID: 36367914 DOI: 10.1109/jbhi.2022.3221639] [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: 02/06/2024]
Abstract
Spasticity is a common complication for patients with stroke, but only few studies investigate the relation between spasticity and voluntary movement. This study proposed a novel automatic system for assessing the severity of spasticity (SS) of four upper-limb joints, including the elbow, wrist, thumb, and fingers, through voluntary movements. A wearable system which combined 19 inertial measurement units and a pressure ball was proposed to collect the kinematic and force information when the participants perform four tasks, namely cone stacking (CS), fast flexion and extension (FFE), slow ball squeezing (SBS), and fast ball squeezing (FBS). Several time and frequency domain features were extracted from the collected data, and two feature selection approaches based on recursive feature elimination were adopted to select the most influential features. The selected features were input into five machine learning techniques for assessing the SS for each joint. The results indicated that using CS task to assess the SS of elbow and fingers and using FBS task to assess the SS of thumb and wrist can reach the highest weighted-average F1-score. Furthermore, the study also concluded that FBS is the optimal task for assessing all the four upper-limb joints. The overall result shown that the proposed automatic system can assess four upper-limb joints through voluntary movements accurately, which is a breakthrough of finding the relation between spasticity and voluntary movement.
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Martino Cinnera A, Picerno P, Bisirri A, Koch G, Morone G, Vannozzi G. Upper limb assessment with inertial measurement units according to the international classification of functioning in stroke: a systematic review and correlation meta-analysis. Top Stroke Rehabil 2024; 31:66-85. [PMID: 37083139 DOI: 10.1080/10749357.2023.2197278] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 03/24/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVE To investigate the usefulness of inertial measurement units (IMUs) in the assessment of motor function of the upper limb (UL) in accordance with the international classification of functioning (ICF). DATA SOURCES PubMed; Scopus; Embase; WoS and PEDro databases were searched from inception to 1 February 2022. METHODS The current systematic review follows PRISMA recommendations. Articles including IMU assessment of UL in stroke individuals have been included and divided into four ICF categories (b710, b735, b760, d445). We used correlation meta-analysis to pool the Fisher Z-score of each correlation between kinematics and clinical assessment. RESULTS A total of 35 articles, involving 475 patients, met the inclusion criteria. In the included studies, IMUs have been employed to assess the mobility of joint functions (n = 6), muscle tone functions (n = 4), control of voluntary movement functions (n = 15), and hand and arm use (n = 15). A significant correlation was found in overall meta-analysis based on 10 studies, involving 213 subjects: (r = 0.69) (95% CI: 0.69/0.98; p < 0.001) as in the d445 (r = 0.71) and b760 (r = 0.64) ICF domains, with no heterogeneity across the studies. CONCLUSION The literature supports the integration of IMUs and conventional clinical assessment in functional evaluation of the UL after a stroke. The use of a limited number of wearable sensors can provide additional kinematic features of UL in all investigated ICF domains, especially in the ADL tasks when a strong correlation with clinical evaluation was found.
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Affiliation(s)
- Alex Martino Cinnera
- Scientific Institute for Research, Hospitalization and Health Care IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Pietro Picerno
- SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "eCampus", Novedrate, Italy
| | | | - Giacomo Koch
- Department of Neuroscience and Rehabilitation, University of Ferrara, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Giuseppe Vannozzi
- Scientific Institute for Research, Hospitalization and Health Care IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
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Adeel M, Peng CW, Lee IJ, Lin BS. Prediction of Spasticity through Upper Limb Active Range of Motion in Stroke Survivors: A Generalized Estimating Equation Model. Bioengineering (Basel) 2023; 10:1273. [PMID: 38002397 PMCID: PMC10669379 DOI: 10.3390/bioengineering10111273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND We aim to study the association between spasticity and active range of motion (ROM) during four repetitive functional tasks such as cone stacking (CS), fast flexion-extension (FFE), fast ball squeezing (FBS), and slow ball squeezing (SBS), and predicted spasticity models. METHODS An experimental study with control and stroke groups was conducted in a Medical Center. A total of sixty-four participants, including healthy control (n = 22; average age (years) = 54.68 ± 9.63; male/female = 12/10) and chronic stroke survivors (n = 42; average age = 56.83 ± 11.74; male/female = 32/10) were recruited. We employed a previously developed smart glove device mounted with multiple inertial measurement unit (IMU) sensors on the upper limbs of healthy and chronic stroke individuals. The recorded ROMs were used to predict subjective spasticity through generalized estimating equations (GEE) for the affected side. RESULTS The models have significant (p ≤ 0.05 *) prediction of spasticity for the elbow, thumb, index, middle, ring, and little fingers. Overall, during SBS and FFE activities, the maximum number of upper limb joints attained the greater average ROMs. For large joints, the elbow during CS and the wrist during FFE have the highest average ROMs, but smaller joints and the wrist have covered the highest average ROMs during FFE, FBS, and SBS activities. CONCLUSIONS Thus, it is concluded that CS can be used for spasticity assessment of the elbow, FFE for the wrist, and SBS, FFE, and FBS activities for the thumb and finger joints in chronic stroke survivors.
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Affiliation(s)
- Muhammad Adeel
- The Master Program in Smart Healthcare Management, International College of Sustainability Innovations, National Taipei University, New Taipei City 237303, Taiwan;
- School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 11031, Taiwan;
| | - Chih-Wei Peng
- School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 11031, Taiwan;
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 11031, Taiwan
| | - I-Jung Lee
- College of Electrical Engineering and Computer Science, National Taipei University, New Taipei City 237303, Taiwan;
| | - Bor-Shing Lin
- Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, Taiwan
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Yee J, Low CY, Mohamad Hashim N, Che Zakaria NA, Johar K, Othman NA, Chieng HH, Hanapiah FA. Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision. Diagnostics (Basel) 2023; 13:diagnostics13040739. [PMID: 36832227 PMCID: PMC9955808 DOI: 10.3390/diagnostics13040739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 01/28/2023] [Accepted: 01/31/2023] [Indexed: 02/17/2023] Open
Abstract
The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical-SVM-RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56-81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction.
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Affiliation(s)
- Jingye Yee
- Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Malaysia
| | - Cheng Yee Low
- Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja 86400, Malaysia
- Correspondence:
| | | | | | - Khairunnisa Johar
- College of Engineering, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
| | - Nurul Atiqah Othman
- College of Engineering, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
| | - Hock Hung Chieng
- Department of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kampar 31900, Malaysia
| | - Fazah Akhtar Hanapiah
- Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh 47000, Malaysia
- Daehan Rehabilitation Hospital Putrajaya, Putrajaya 62502, Malaysia
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Lin BS, Lee IJ, Hsiao PC, Yang SY, Chen CY, Lee SH, Huang YF, Yen MH, Hu YH. Design of a Multi-Sensor System for Exploring the Relation between Finger Spasticity and Voluntary Movement in Patients with Stroke. SENSORS (BASEL, SWITZERLAND) 2022; 22:7212. [PMID: 36236314 PMCID: PMC9573204 DOI: 10.3390/s22197212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
A novel wearable multi-sensor data glove system is developed to explore the relation between finger spasticity and voluntary movement in patients with stroke. Many stroke patients suffer from finger spasticity, which is detrimental to their manual dexterity. Diagnosing and assessing the degrees of spasticity require neurological testing performed by trained professionals to estimate finger spasticity scores via the modified Ashworth scale (MAS). The proposed system offers an objective, quantitative solution to assess the finger spasticity of patients with stroke and complements the manual neurological test. In this work, the hardware and software components of this system are described. By requiring patients to perform five designated tasks, biomechanical measurements including linear and angular speed, acceleration, and pressure at every finger joint and upper limb are recorded, making up more than 1000 features for each task. We conducted a preliminary clinical test with 14 subjects using this system. Statistical analysis is performed on the acquired measurements to identify a small subset of features that are most likely to discriminate a healthy patient from patients suffering from finger spasticity. This encouraging result validates the feasibility of this proposed system to quantitatively and objectively assess finger spasticity.
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Affiliation(s)
- Bor-Shing Lin
- Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, Taiwan
| | - I-Jung Lee
- Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, Taiwan
- College of Electrical Engineering and Computer Science, National Taipei University, New Taipei City 237303, Taiwan
| | - Pei-Chi Hsiao
- Department of Physical Medicine and Rehabilitation, Chi-Mei Medical Center, Tainan 71004, Taiwan
| | - Shu-Yu Yang
- Department of Physical Medicine and Rehabilitation, Chi-Mei Medical Center, Tainan 71004, Taiwan
| | - Chen-Yu Chen
- Department of Physical Medicine and Rehabilitation, Chi-Mei Medical Center, Tainan 71004, Taiwan
| | - Si-Huei Lee
- Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yu-Fang Huang
- Department of Physical Medicine and Rehabilitation, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Mao-Hsu Yen
- Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan
| | - Yu Hen Hu
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
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Li J, Liang T, Zeng Z, Xu P, Chen Y, Guo Z, Liang Z, Xie L. Motion intention prediction of upper limb in stroke survivors using sEMG signal and attention mechanism. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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