Displaying publications 21 - 31 of 31 in total

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  1. Naeem J, Hamzaid NA, Islam MA, Azman AW, Bijak M
    Med Biol Eng Comput, 2019 Jun;57(6):1199-1211.
    PMID: 30687901 DOI: 10.1007/s11517-019-01949-4
    Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity. Graphical abstract ᅟ.
  2. Ibitoye MO, Hamzaid NA, Abdul Wahab AK, Hasnan N, Olatunji SO, Davis GM
    Comput Biol Med, 2020 02;117:103614.
    PMID: 32072969 DOI: 10.1016/j.compbiomed.2020.103614
    BACKGROUND AND OBJECTIVE: Using traditional regression modelling, we have previously demonstrated a positive and strong relationship between paralyzed knee extensors' mechanomyographic (MMG) signals and neuromuscular electrical stimulation (NMES)-assisted knee torque in persons with spinal cord injuries. In the present study, a method of estimating NMES-evoked knee torque from the knee extensors' MMG signals using support vector regression (SVR) modelling is introduced and performed in eight persons with chronic and motor complete spinal lesions.

    METHODS: The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension.

    RESULTS: When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R2), with a Gaussian support vector kernel, the R2 value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively.

    CONCLUSIONS: These results showed good predictive accuracy for SVR modelling, which can be generalized, and suggested that the MMG signals from paralyzed knee extensors are a suitable proxy for the NMES-assisted torque produced during repeated bouts of isometric knee extension tasks. This finding has potential implications for using MMG signals as torque sensors in NMES closed-loop systems and provides valuable information for implementing this method in research and clinical settings.

  3. Ibitoye MO, Hamzaid NA, Abdul Wahab AK, Hasnan N, Olatunji SO, Davis GM
    Sensors (Basel), 2016 Jul 19;16(7).
    PMID: 27447638 DOI: 10.3390/s16071115
    The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.
  4. Jasni F, Hamzaid NA, Mohd Syah NE, Chung TY, Abu Osman NA
    Front Neurosci, 2017;11:230.
    PMID: 28487630 DOI: 10.3389/fnins.2017.00230
    The walking mechanism of a prosthetic leg user is a tightly coordinated movement of several joints and limb segments. The interaction among the voluntary and mechanical joints and segments requires particular biomechanical insight. This study aims to analyze the inter-relationship between amputees' voluntary and mechanical coupled leg joints variables using cyclograms. From this analysis, the critical gait parameters in each gait phase were determined and analyzed if they contribute to a better powered prosthetic knee control design. To develop the cyclogram model, 20 healthy able-bodied subjects and 25 prosthesis and orthosis users (10 transtibial amputees, 5 transfemoral amputees, and 10 different pathological profiles of orthosis users) walked at their comfortable speed in a 3D motion analysis lab setting. The gait parameters (i.e., angle, moment and power for the ankle, knee and hip joints) were coupled to form 36 cyclograms relationship. The model was validated by quantifying the gait disparities of all the pathological walking by analyzing each cyclograms pairs using feed-forward neural network with backpropagation. Subsequently, the cyclogram pairs that contributed to the highest gait disparity of each gait phase were manipulated by replacing it with normal values and re-analyzed. The manipulated cyclograms relationship that showed highest improvement in terms of gait disparity calculation suggested that they are the most dominant parameters in powered-knee control. In case of transfemoral amputee walking, it was identified using this approach that at each gait sub-phase, the knee variables most responsible for closest to normal walking were: knee power during loading response and mid-stance, knee moment and knee angle during terminal stance phase, knee angle and knee power during pre-swing, knee angle at initial swing, and knee power at terminal swing. No variable was dominant during mid-swing phase implying natural pendulum effect of the lower limb between the initial and terminal swing phases. The outcome of this cyclogram adoption approach proposed an insight into the method of determining the causal effect of manipulating a particular joint's mechanical properties toward the joint behavior in an amputee's gait by determining the curve closeness, C, of the modified cyclogram curve to the normal conventional curve, to enable quantitative judgment of the effect of changing a particular parameter in the prosthetic leg gait.
  5. Islam MA, Hamzaid NA, Ibitoye MO, Hasnan N, Wahab AKA, Davis GM
    Clin Biomech (Bristol, Avon), 2018 10;58:21-27.
    PMID: 30005423 DOI: 10.1016/j.clinbiomech.2018.06.020
    BACKGROUND: Investigation of muscle fatigue during functional electrical stimulation (FES)-evoked exercise in individuals with spinal cord injury using dynamometry has limited capability to characterize the fatigue state of individual muscles. Mechanomyography has the potential to represent the state of muscle function at the muscle level. This study sought to investigate surface mechanomyographic responses evoked from quadriceps muscles during FES-cycling, and to quantify its changes between pre- and post-fatiguing conditions in individuals with spinal cord injury.

    METHODS: Six individuals with chronic motor-complete spinal cord injury performed 30-min of sustained FES-leg cycling exercise on two days to induce muscle fatigue. Each participant performed maximum FES-evoked isometric knee extensions before and after the 30-min cycling to determine pre- and post- extension peak torque concomitant with mechanomyography changes.

    FINDINGS: Similar to extension peak torque, normalized root mean squared (RMS) and mean power frequency (MPF) of the mechanomyography signal significantly differed in muscle activities between pre- and post-FES-cycling for each quadriceps muscle (extension peak torque up to 69%; RMS up to 80%, and MPF up to 19%). Mechanomyographic-RMS showed significant reduction during cycling with acceptable between-days consistency (intra-class correlation coefficients, ICC = 0.51-0.91). The normalized MPF showed a weak association with FES-cycling duration (ICC = 0.08-0.23). During FES-cycling, the mechanomyographic-RMS revealed greater fatigue rate for rectus femoris and greater fatigue resistance for vastus medialis in spinal cord injured individuals.

    INTERPRETATION: Mechanomyographic-RMS may be a useful tool for examining real time muscle function of specific muscles during FES-evoked cycling in individuals with spinal cord injury.

  6. Braz GP, Russold MF, Fornusek C, Hamzaid NA, Smith RM, Davis GM
    Med Eng Phys, 2016 11;38(11):1223-1231.
    PMID: 27346492 DOI: 10.1016/j.medengphy.2016.06.007
    This pilot study reports the development of a novel closed-loop (CL) FES-gait control system, which employed a finite-state controller that processed kinematic feedback from four miniaturized motion sensors. This strategy automated the control of knee extension via quadriceps and gluteus stimulation during the stance phase of gait on the supporting leg, and managed the stimulation delivered to the common peroneal nerve (CPN) during swing-phase on the contra-lateral limb. The control system was assessed against a traditional open-loop (OL) system on two sensorimotor 'complete' paraplegic subjects. A biomechanical analysis revealed that the closed-loop control of leg swing was efficient, but without major advantages compared to OL. CL automated the control of knee extension during the stance phase of gait and for this reason was the method of preference by the subjects. For the first time, a feedback control system with a simplified configuration of four miniaturized sensors allowed the addition of instruments to collect the data of multiple physiological and biomechanical variables during FES-evoked gait. In this pilot study of two sensorimotor complete paraplegic individuals, CL ameliorated certain drawbacks of current OL systems - it required less user intervention and accounted for the inter-subject differences in their stimulation requirements.
  7. Mohamad NZ, Hamzaid NA, Davis GM, Abdul Wahab AK, Hasnan N
    Sensors (Basel), 2017 Jul 14;17(7).
    PMID: 28708068 DOI: 10.3390/s17071627
    A mechanomyography muscle contraction (MC) sensor, affixed to the skin surface, was used to quantify muscle tension during repetitive functional electrical stimulation (FES)-evoked isometric rectus femoris contractions to fatigue in individuals with spinal cord injury (SCI). Nine persons with motor complete SCI were seated on a commercial muscle dynamometer that quantified peak torque and average torque outputs, while measurements from the MC sensor were simultaneously recorded. MC-sensor-predicted measures of dynamometer torques, including the signal peak (SP) and signal average (SA), were highly associated with isometric knee extension peak torque (SP: r = 0.91, p < 0.0001), and average torque (SA: r = 0.89, p < 0.0001), respectively. Bland-Altman (BA) analyses with Lin's concordance (ρC) revealed good association between MC-sensor-predicted peak muscle torques (SP; ρC = 0.91) and average muscle torques (SA; ρC = 0.89) with the equivalent dynamometer measures, over a range of FES current amplitudes. The relationship of dynamometer torques and predicted MC torques during repetitive FES-evoked muscle contraction to fatigue were moderately associated (SP: r = 0.80, p < 0.0001; SA: r = 0.77; p < 0.0001), with BA associations between the two devices fair-moderate (SP; ρC = 0.70: SA; ρC = 0.30). These findings demonstrated that a skin-surface muscle mechanomyography sensor was an accurate proxy for electrically-evoked muscle contraction torques when directly measured during isometric dynamometry in individuals with SCI. The novel application of the MC sensor during FES-evoked muscle contractions suggested its possible application for real-world tasks (e.g., prolonged sit-to-stand, stepping,) where muscle forces during fatiguing activities cannot be directly measured.
  8. Hamdan PNF, Hamzaid NA, Hasnan N, Abd Razak NA, Razman R, Usman J
    Sci Rep, 2024 Mar 18;14(1):6451.
    PMID: 38499594 DOI: 10.1038/s41598-024-56955-w
    Literature has shown that simulated power production during conventional functional electrical stimulation (FES) cycling was improved by 14% by releasing the ankle joint from a fixed ankle setup and with the stimulation of the tibialis anterior and triceps surae. This study aims to investigate the effect of releasing the ankle joint on the pedal power production during FES cycling in persons with spinal cord injury (SCI). Seven persons with motor complete SCI participated in this study. All participants performed 1 min of fixed-ankle and 1 min of free-ankle FES cycling with two stimulation modes. In mode 1 participants performed FES-evoked cycling with the stimulation of quadriceps and hamstring muscles only (QH stimulation), while Mode 2 had stimulation of quadriceps, hamstring, tibialis anterior, and triceps surae muscles (QHT stimulation). The order of each trial was randomized in each participant. Free-ankle FES cycling offered greater ankle plantar- and dorsiflexion movement at specific slices of 20° crank angle intervals compared to fixed-ankle. There were significant differences in the mean and peak normalized pedal power outputs (POs) [F(1,500) = 14.03, p 
  9. Hamzaid NA, Manaf H, Azmi NL, Milosevic M, Spaich EG, Yoshida K, et al.
    Artif Organs, 2024 Apr;48(4):421-425.
    PMID: 38339848 DOI: 10.1111/aor.14720
    The annual conference of the International Functional Electrical Stimulation Society (IFESS) was held in conjunction with the 7th RehabWeek Congress, from September 24 to 28, 2023 at the Resorts World Convention Centre on Sentosa Island, in Singapore. The Congress was a joint meeting of the International Consortium on Rehabilitation Technology (ICRT) together with 10 other societies in the field of assistive technology and rehabilitation engineering. The conference features comprehensive blend of technical and clinical context of FES, a sustained value the society has offered over many years. The cross- and inter- disciplinary approach of medicine, engineering, and science practiced in the FES community had enabled vibrant interaction, creation, and development of impactful and novel contributions to the field of FES, translating FES directly into highly relevant and sustainable solutions for the users.
  10. El-Sayed AM, Abo-Ismail A, El-Melegy MT, Hamzaid NA, Osman NA
    Sensors (Basel), 2013 May 07;13(5):5826-40.
    PMID: 23653051 DOI: 10.3390/s130505826
    Piezoelectric bimorphs have been used as a micro-gripper in many applications, but the system might be complex and the response performance might not have been fully characterized. In this study the dynamic characteristics of bending piezoelectric bimorphs actuators were theoretically and experimentally investigated for micro-gripping applications in terms of deflection along the length, transient response, and frequency response with varying driving voltages and driving signals. In addition, the implementation of a parallel micro-gripper using bending piezoelectric bimorphs was presented. Both fingers were actuated separately to perform mini object handling. The bending piezoelectric bimorphs were fixed as cantilevers and individually driven using a high voltage amplifier and the bimorph deflection was measured using a non contact proximity sensor attached at the tip of one finger. The micro-gripper could perform precise micro-manipulation tasks and could handle objects down to 50 µm in size. This eliminates the need for external actuator extension of the microgripper as the grasping action was achieved directly with the piezoelectric bimorph, thus minimizing the weight and the complexity of the micro-gripper.
  11. Ibitoye MO, Hamzaid NA, Hasnan N, Abdul Wahab AK, Islam MA, Kean VS, et al.
    Med Eng Phys, 2016 Aug;38(8):767-75.
    PMID: 27289541 DOI: 10.1016/j.medengphy.2016.05.012
    The interaction between muscle contractions and joint loading produces torques necessary for movements during activities of daily living. However, during neuromuscular electrical stimulation (NMES)-evoked contractions in persons with spinal cord injury (SCI), a simple and reliable proxy of torque at the muscle level has been minimally investigated. Thus, the purpose of this study was to investigate the relationships between muscle mechanomyographic (MMG) characteristics and NMES-evoked isometric quadriceps torques in persons with motor complete SCI. Six SCI participants with lesion levels below C4 [(mean (SD) age, 39.2 (7.9) year; stature, 1.71 (0.05) m; and body mass, 69.3 (12.9) kg)] performed randomly ordered NMES-evoked isometric leg muscle contractions at 30°, 60° and 90° knee flexion angles on an isokinetic dynamometer. MMG signals were detected by an accelerometer-based vibromyographic sensor placed over the belly of rectus femoris muscle. The relationship between MMG root mean square (MMG-RMS) and NMES-evoked torque revealed a very high association (R(2)=0.91 at 30°; R(2)=0.98 at 60°; and R(2)=0.97 at 90° knee angles; P<0.001). MMG peak-to-peak (MMG-PTP) and stimulation intensity were less well related (R(2)=0.63 at 30°; R(2)=0.67 at 60°; and R(2)=0.45 at 90° knee angles), although were still significantly associated (P≤0.006). Test-retest interclass correlation coefficients (ICC) for the dependent variables ranged from 0.82 to 0.97 for NMES-evoked torque, between 0.65 and 0.79 for MMG-RMS, and from 0.67 to 0.73 for MMG-PTP. Their standard error of measurements (SEM) ranged between 10.1% and 31.6% (of mean values) for torque, MMG-RMS and MMG-PTP. The MMG peak frequency (MMG-PF) of 30Hz approximated the stimulation frequency, indicating NMES-evoked motor unit firing rate. The results demonstrated knee angle differences in the MMG-RMS versus NMES-isometric torque relationship, but a similar torque related pattern for MMG-PF. These findings suggested that MMG was well associated with torque production, reliably tracking the motor unit recruitment pattern during NMES-evoked muscle contractions. The strong positive relationship between MMG signal and NMES-evoked torque production suggested that the MMG might be deployed as a direct proxy for muscle torque or fatigue measurement during leg exercise and functional movements in the SCI population.
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