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Evaluating Marker-Based and Markerless Motion Capture Systems in Reach-to-Grasp Task
(2024-01-01)
Liangsorn, Natsakorn
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Tretriluxana, Suradej
This study evaluated the accuracy of marker-based versus markerless motion capture systems in tracking reach-to-grasp movements, using an electromagnetic tracking system as the gold standard. Reach-to-grasp tasks are fundamental activity in daily activities and often affected by neurological conditions like stroke, highlighting the importance of accurate measurement in rehabilitation research. The findings show that the marker-based system consistently outperformed the markerless system, with significantly lower root mean square errors (RMSE) across all axes. The marker-based system achieved an average RMSE of 4.55 millimeters (mm), demonstrating clinical-level accuracy, while the markerless system averaged 23.64 mm, indicating limitations in its ability to track movements with high accuracy.
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Effect of Class Weights on Imbalanced Classes in Bi-directional LSTM Training for Sleep Apnea Classification
(2024-01-01)
Jansri, Ukkrit
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Tretriluxana, Suradej
Sleep apnea, which is defined as the repetitive cessations of breathing during sleep, is the common disorder worldwide. The cost and the process of sleep test to obtain the polysomnogram is not optimal for sleep apnea screening in the large population. A deep learning model was developed to classify the normal and apnea events in a single time-series signal exported from the US National Institute of Health (NIH) sponsored database. Our challenge was to train the model with imbalanced dataset between normal and abnormal respiratory events. Three different methods, Synthetic Minority Over-sampling Technique (SMOTE), Random Under-Sampling (RUS), and the Class Weights (CW) were chosen to improve the model performance over the original data on five selected signals from polysomnographic dataset. The binary classification outputs were evaluated by four metrics. Our results showed (1) Matthews Correlation Coefficient was highest (MCC= 0.1385) in the Class Weights method on the nasal airflow signal. (2) Cohen's Kappa score, was highest (k= 0.0819) in SMOTE technique on the abdominal signal, followed by the Class Weights method on the abdominal signal (k= 0.0687) and RUS technique on nasal airflow signal (k= 0.0441). (3) F1-score was highest (F1= 11.89%) in SMOTE technique on the abdominal signal, followed by the Class Weights method on nasal airflow signal (F1 = 11.17%) and RUS technique on nasal airflow signal (F1= 9.16%). The findings suggest that the Class Weights method on nasal airflow and the Class Weights method on abdominal signal were the two combinations to be used in the DL model.
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Effects of repetitive transcranial magnetic stimulation combined with action-observation-execution on social interaction and communication in autism spectrum disorder: Feasibility study
(2023-04-01)
Kaokhieo, Jirapimon
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Tretriluxana, Jarugool
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Chaiyawat, Pakaratee
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Siripornpanich, Vorasith
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Permpoonputtana, Kannika
Objective: To investigate the feasibility of a combined high-frequency rTMS (HF-rTMS) with action observation and execution (AOE) on social interaction and communication in children with Autistic Spectrum Disorder (ASD). Materials and methods: Fifteen children underwent 10 sessions of 5-Hz HF-rTMS on the right inferior frontal gyrus combined with AOE. An experimental group received the real HF-rTMS while the control group received the sham one. For the AOE protocol, they were instructed to watch and imitate a video showing the procedure, including reaching and grasping tasks, gustatory tasks, and facial expressions. Their behavioural outcomes were evaluated using the Vineland Adaptive Behaviour Scale (VABS) and electroencephalograms (EEGs) recorded at three time points: baseline, immediately after each treatment, and at the 1-week follow-up after the 10th treatment. Results: There was a reduction in the VABS subitem scores of the experimental group, including the receptive, expressive, domestic, and community scores but no such reductions were observed in the control group. For the EEG, the beta rhythm at C3 and C4 increased in the experimental group. Additionally, positive correlations were observed between changes in the scores for the expressive subitem and changes in the beta rhythm on the C4 electrode at baseline and immediately after treatment in the experimental group. The control group showed no significant differences in any items for both observation and imitation times. Conclusion: Ten sessions of HF-rTMS combined with AOE could improve both the subitems of communication and daily living skills domain in children aged 7–12 years with ASD. Although it is still inconclusive, this behavioural improvement may be partly attributable to increased cortical activity, as evidenced by beta rhythms.
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Comparing the performances of deep learning model with different signals underlying resampling techniques to classify sleep apnea
(2023-01-01)
Jansri, Ukkrit
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Tretriluxana, Suradej
Millions of people around the world are suffering from long term Sleep Apnea. Full scale sleep test is costly and time-consuming. This paper, using deep learning model, chose a single candidate signal from multi-channel polysomnogram data for sleep apnea screening. Nature of data, however, shows an imbalance class of dataset between normal and apneic events. To increase the binary classification output performance, two resampling techniques; Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-Sampling (RUS), were employed in Bidirectional Long Short-Term Memory (Bi-LSTM) model training. One hundred polysomnography (PSG) records were randomly selected from the Multi-Ethic of Atherosclerosis (MESA) database in this study. They were trained under three conditions; original, SMOTE and RUS datasets. Our results showed (1) Cohen's kappa score was greater in resampling (SMOTE, RUS) datasets than original one. (2) Between the resampling techniques, metrices in SMOTE were better than ones in RUS. (3) Within SMOTE, the abdominal belt was the best among other signals with Cohen's kappa score of 0.2078 and 58.99% in F1-score. These findings suggested that abdominal belt was the best candidate signal for sleep apnea screening.
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Task Oriented Training Activities Post Stroke Will Produce Measurable Alterations in Brain Plasticity Concurrent with Skill Improvement
(2022-01-01)
Rungseethanakul, Somchanok
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Tretriluxana, Jarugool
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Piriyaprasarth, Pagamas
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Pakaprot, Narawut
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Jitaree, Khanitha
Background: Task-oriented training with upper extremity (UE) skilled movements has been established as a method to regain function post stroke. Although improved UE function has been shown after this type of therapy, there is minimal evidence that brain plasticity is associated with this training. The accelerated skill acquisition program (ASAP) is an example of an approach for promoting UE function using targeting movements. Objective: To investigate the effects of a single 2-hour session of ASAP in individuals with stroke on measures of brain plasticity as represented by corticospinal excitability (CE) and determine associations with reach-to-grasp (RTG) performance. Methods: Eighteen post-acute stroke patients were randomized to two groups. Experimental group (n = 9) underwent ASAP for 2 hours, while the control group (n = 9) received dose equivalent usual and customary care. Both groups were evaluated for CE and RTG performance prior to the session and then four times after training: immediately, 1 day, 6 days, and 12 days. Results: Significant alterations in CE were found in the peak-to-peak of Motor Evoked Potential amplitude of elbow and wrist extensor muscles in the lesioned hemisphere. The experimental group also demonstrated improved execution (shortened total movement time, TMT), feed-forward mechanism (deceleration time, DT) and planning (lengthened relative time to maximum hand aperture, RTApmax) compared to the control group Conclusion: Alterations in brain plasticity occur concurrently with improvements in RTG performance in post-acute stroke patients with mild impairment after a single 2-hour session of task-oriented training and persist for at least 12 days.
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Effect of Resampling Techniques on Deep Learning Model Training in Sleep Apnea Classification
(2022-01-01)
Jansri, Ukkrit
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Tretriluxana, Suradej
This study is using deep learning model to classify the respiratory events of Sleep Disordered Breathing (SDB) data. Our pilot results showed the missed identification in some classes even the total accuracy is high. This is the result of unbalanced training dataset given to the model. Two different resampling techniques; Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-Sampling (RUS), were introduced to balance the data. One hundred overnight nasal airflow signals were randomly selected from NIH funded polysomnography database. They were used to train and test these two algorithms with Bi-directional Long Short-Term Memory (Bi-LSTM) model. The results showed greater agreement index when compared between with and without data resampling process. However, SMOTE in sum performed better than RUS (93.72% vs 70.01% in overall accuracy and 0.91 vs 0.55 in Cohen's kappa). It demonstrates that the over-sampling technique is more powerful than under-sampling one. Other resampling techniques will be investigated to make the robust conclusion.
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Reach-to-Grasp Motion Tracking System Using Single Smart Phone Camera
(2022-01-01)
Thongprasan, Sahawatchara
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Liangsorn, Natsakorn
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Raden-Ahmad, Chafik
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Kongkaew, Kotchakorn
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Tretriluxana, Jarugool
Assessment of human movement is necessary for physical therapy management. This article presents a development of motion tracking system for human Upper Extremity (UE) function analysis. We proposed the optical motion capture system made by a single smart phone camera. It was used to capture the Reach-to-Grasp (RTG) movement of participants in sitting position. Image processing were used to detect color markers placed on chosen hand anatomical landmarks. With our simple camera calibration technique, the 3D coordinates of hand movement were obtained. Two clinical parameters, grasp aperture and hand transport velocity were computed. These results were compared with the outputs, collected at the same time, from the higher accuracy Electromagnetic Motion (EM) tracking system. Qualitatively, the result patterns from two systems were parallel to each other. Our ongoing work is to improve the algorithms according to the feedback from clinicians. This system may provide implication for physical therapist to assess the clients' movement in the clinical setting.
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Automatic Sleep Data Scoring by Artificial Intelligence: A Pilot Study in Thai Population
(2021-04-01)
Jansri, Ukkrit
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Chirakalwasan, Naricha
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Chaitusaney, Busarakum
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Busayakanon, Supasuta
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Khongjui, Thamonwan
Sleep apnea, a sleep-disordered breathing (SDB), is defined as repeatedly intermittent cessation of breathing during sleep. It causes various life-threatening diseases. The American Academy of Sleep Medicine (AASM) releases the manual for sleep data scoring. Patients with SDB are prescribed to be monitored at the sleep clinic where several physiological data are recorded, called polysomnogram (PSG). The massive PSG data must be scored by the well-trained expert before being diagnosed by the physician. Our research is to use the Artificial Intelligence (AI) in sleep data scoring, particularly in respiratory events detection. Three ready-made Convolution Neural Networks (CNN); AlexNet, ResNet-50, and VGG-16, with transfer learning were applied to classify 5 overnight PSG data from Chulalongkorn hospital. Our preliminary results showed that all networks provide higher classification result in European Data Format (EDF) than in the text (ASCII) formats (71% vs 54%). The ResNet-50 model structure performed better than the other two networks on both data formats. As expected, the visualized (EDF) data is better than the unconditioned (ASCII) data. Our future development is modifying learning model to increase the scoring performance from more recruited PSG data.
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Age-related changes in reach-to-grasp movements with partial visual occlusion
(2019-08-01)
Runnarong, Nuttakarn
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Tretriluxana, Jarugool
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Waiyasil, Watinee
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Sittisupapong, Preeyanuch
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Tretriluxana, Suradej
This study investigated the influence of age and visual occlusion on fast reach-to-grasp movements. The effect of visual occlusion on reach-to-grasp movement was examined using a task that heavily relies on feed-forward control. Three groups of healthy adults aged 22, 49 and 65 on average performed fast reach-to-grasp movements with full visual and partial visual occlusion conditions of the hand during the initial part of movement. Regarding the effect of age, the all parameters of reach-to-grasp movement were deteriorated with age, except relative time to maximum velocity and spatial coordination. Regarding the effect of visual condition, participants reached with prolonged movement time, lower peak velocity, and later occurrences of peak velocity and peak aperture, as well as decrease in spatial coordination. Regarding the effect of age on visual condition, visual occlusion resulted in a longer movement time and delayed time to maximum velocity in middle-aged and older groups compared to full vision, but the difference was not observed in the younger groups. Conclusion: Reach-to-grasp performance deteriorated with age and the performance was affected when vision of the hand at initial movement was occluded. Overall, movement performance in middle-aged and older adults was affected by visual occlusion, whereas it was unaffected in younger adults. The results indicate that visual feedback of the hand at initial movement is important to control reach-to-grasp movement of middle-aged and older adults during real tasks.
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The persisted effects of low-frequency repetitive transcranial magnetic stimulation to augment task-specific induced hand recovery following subacute stroke: Extended study
(2018-12-01)
Tretriluxana, Jarugool
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Thanakamchokchai, Jenjira
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Jalayondeja, Chutima
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Pakaprot, Narawut
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Tretriluxana, Suradej
Objective To examine the long-term effects of the low-frequency repetitive transcranial magnetic stimulation (LFrTMS) combined with task-specific training on paretic hand function following subacute stroke. Methods Sixteen participants were randomly selected and grouped into two: the experimental group (real LFrTMS) and the control group (sham LF-rTMS). All the 16 participants were then taken through a 1-hour taskspecific training of the paretic hand. The corticospinal excitability (motor evoke potential [MEP] amplitude) of the non-lesioned hemisphere, and the paretic hand performance (Wolf Motor Function Test total movement time [WMFT-TMT]) were evaluated at baseline, after the LF-rTMS, immediately after task-specific training, 1 and 2 weeks after the training. Results Groups comparisons showed a significant difference in the MEP after LF-rTMS and after the training. Compared to the baseline, the MEP of the experimental group significantly decreased after LF-rTMS and after the training and that effect was maintained for 2 weeks. Group comparisons showed significant difference in WMFT-TMT after the training. Only in the experimental group, the WMFT-TMT of the can lifting item significantly reduced compared to the baseline and the effect was sustained for 2 weeks. Conclusion The results of this study established that the improvement in paretic hand after task-specific training was enhanced by LF-rTMS and it persisted for at least 2 weeks.

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