Faculty Publications

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Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses
(Elsevier Ltd, 2019) Powar, O.S.; Chemmangat, K.
Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive. However, there has been limited attention to the factors that influence the clinical translation of this technology. A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement. To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of force for six hand grips. The results showed that DTW trained at a single force level achieved an average classification accuracy of 60 ± 9%, 70 ± 8%, and 60 ± 7% at the low, medium and high force levels respectively across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification accuracy when trained at a low force level when compared to the traditional time-dependent power spectrum descriptors (TD-PSD) method. © 2019 Elsevier Ltd
A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis
(Institute of Physics, 2024) Kumar Koppolu, P.; Chemmangat, K.
Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
IMU-based segmental root mean square analysis of gait in individuals with cerebellar ataxia: a pilot cross-sectional study
(Nature Research, 2025) Mendonca, J.; Joshua, A.M.; Shetty, S.; Chemmangat, K.; Krishnan, S.; Kumar, K.V.; Misri, Z.; Pai, R.; Pai, S.
Cerebellar ataxia (CA) affects limb movement, balance, and gait. Subjective rating scales like Scale for the Assessment and Rating of Ataxia (SARA) may underestimate gait severity. Inertial measurement units (IMUs) offer an objective gait analysis. Impaired trunk control might compromise gait performance and stability in individuals with ataxia. This study quantified trunk kinematics and gait parameters using Root Mean Square (RMS) values, comparing CA to healthy individuals. Ten CA cases and twenty healthy controls were recruited. Six IMU sensors positioned at anatomical landmarks recorded data via two ESP32 microcontrollers using Wi-Fi. Participants walked a 10-meter path at a self-selected pace. RMS mean linear and angular velocity and angular deviation were calculated. Individuals with CA showed decreased mediolateral linear acceleration at the left shoulder (p = 0.001) and an increased vertical linear acceleration at the right ankle (p = 0.015), left shoulder (p = 0.028), and back (p = 0.019). Total angular velocity was lower at the right shoulder (p = 0.017), left shoulder (p = 0.005), back (p = 0.002), and both ankles (right: p = 0.001; left: p = 0.001). The correlation between IMU-derived features and SARA-gait score in the CA group was not statistically significant (all p > 0.05), except for the right shoulder’s mediolateral angular velocity (p = 0.046). Both ankle segments’ angular deviations (right: p = 0.001; left: p = 0.006) were reduced. The CA group revealed reduced RMS linear and angular velocities. IMU-based trunk and gait analysis provides a more objective method that would help in planning targeted rehabilitation treatments. Trial registration: The study was approved by the Institutional Ethics Committee (IEC), Kasturba Medical College, Mangalore, Manipal Academy of Higher Education (IEC KMC MLR 12/2023/483) on 21st December 2023 and the Clinical Trial Registration (CTRI/2024/07/070614) on July 15th, 2024. © The Author(s) 2025.

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