Sourav Chandra

Bio: Sourav Chandra is an academic researcher from Northwestern University. The author has contributed to research in topics: Electromyography & Isometric exercise. The author has an hindex of 4, co-authored 19 publications receiving 48 citations. Previous affiliations of Sourav Chandra include Indian Institute of Technology Madras & Jadavpur University.

Performance Evaluation of a Wearable Tattoo Electrode Suitable for High-Resolution Surface Electromyogram Recording

Abstract: Objective : High-density surface electromyography (HD-sEMG) has been utilized extensively in neuromuscular research. Despite its potential advantages, limitations in electrode design have largely prevented widespread acceptance of the technology. Commercial electrodes have limited spatial fidelity, because of a lack of sharpness of the signal, and variable signal stability. We demonstrate here a novel tattoo electrode that addresses these issues. Our dry HD electrode grid exhibits remarkable deformability which ensures superior conformity with the skin surface, while faithfully recording signals during different levels of muscle contraction. Method: We fabricated a 4 cm×3 cm tattoo HD electrode grid on a stretchable electronics membrane for sEMG applications. The grid was placed on the skin overlying the biceps brachii of healthy subjects, and was used to record signals for several hours while tracking different isometric contractions. Results: The sEMG signals were recorded successfully from all 64 electrodes across the grid. These electrodes were able to faithfully record sEMG signals during repeated contractions while maintaining a stable baseline at rest. During voluntary contractions, broad EMG frequency content was preserved, with accurate reproduction of the EMG spectrum across the full signal bandwidth. Conclusion: The tattoo grid electrode can potentially be used for recording high-density sEMG from skin overlying major limb muscles. Layout programmability, good signal quality, excellent baseline stability, and easy wearability make this electrode a potentially valuable component of future HD electrode grid applications. Significance: The tattoo electrode can facilitate high fidelity recording in clinical applications such as tracking the evolution and time-course of challenging neuromuscular degenerative disorders.

Dominant component in muscle fatigue induced hand tremor during laparoscopic surgical manipulation

Abstract: Accuracy of laparoscopic surgery gets affected by the hand tremor of the surgeons. Though cognitive load is inevitable in such activity which promotes tremor, muscle fatigue induced tremor is significant among the most important sources of tremor. Characteristic of fatigue induced hand tremor and its dominant directional properties are reported in this work. For a fixed laparoscopic tool grip with temporally synchronized predefined task protocols, characteristics of fatigue induced tremors have been studied. Dominant component of tremor was found to be in the sagittal plane in case of both static and dynamic tasks. In order to relate it with the muscle fatigue level, spectral properties of surface electromyography (SEMG) were also investigated simultaneously. A study of transient effect on tool positioning was also included, which conjointly advocates the other experimental results on fatigue induced hand tremor as well.

Muscle Fatigue Induced Hand Tremor Clustering in Dynamic Laparoscopic Manipulation

Abstract: Differentiating muscle fatigue induced hand tremor of surgeons into different discernible levels is important in laparoscopic surgery. Systematic clustering can be used as a method to assess the risk of hand tremor which can largely affect the surgical performance. The prime challenges lying here are the detection of fatigue onset and classification of fatigue induced tremor level in dynamic laparoscopic tool manipulation. Conventionally, muscle fatigue is assessed with frequency domain analysis of the surface electromyography (sEMG) signal, where the detection process is predominantly valid only for isometric contraction of muscles. Conventional methods cannot be used for assessment of fatigue level in case of dynamic activities as the task itself modulates the frequency content of the myoelectric response. In this paper, we have proposed a novel polynomial Hammerstein model-based clustering of fatigue induced tremor, employing sEMG, and joint torques. The sEMG signal, containing muscle fatigue information, gets fused in this model dynamically through a Kalman filter. Model parameter-based clustering of the fatigue induced tremor level was implemented on eight subjects. Optimal number of cluster centers were found to be appropriately coherent with the fatigue inducing task epochs of the experiment. In spite of the subjective variations, the model parameter-based clustering method was able to differentiate among the fatigue-inducing tasks for all the subjects. We have concluded that this model-based clustering can successfully differentiate between different levels of the fatigue induced tremor in dynamic activity of laparoscopic tool manipulation.

Precise quantification of the time course of voluntary activation capacity following Botulinum toxin injections in the biceps brachii muscles of chronic stroke survivors

Abstract: Spasticity is a key motor impairment that affects many hemispheric stroke survivors. Intramuscular botulinum toxin (BT) injections are used widely to clinically manage spasticity-related symptoms in stroke survivors by chemically denervating muscle fibers from their associated motor neurons. In this study, we sought to understand how BT affects muscle activation, motor unit composition and voluntary force generating capacity over a time period of 3 months. Our purpose was to characterize the time course of functional changes in voluntary muscle activity in stroke survivors who are undergoing BT therapy as part of their physician-prescribed clinical plan. Our assessment of the effects of BT was based on the quantification of surface electromyogram (sEMG) recordings in the biceps brachii (BB), an upper arm muscle and of voluntary contraction force. We report here on voluntary force and sEMG responses during isometric elbow contractions across consecutive recording sessions, spread over 12 weeks in three segments, starting with a preliminary session performed just prior to the BT injection. At predetermined time points, we conducted additional clinical assessments and we also recorded from the contralateral limbs of our stroke cohort. Eight subjects were studied for approximately 86 experimental recording sessions on both stroke-affected and contralateral sides. We recorded an initial reduction in force and sEMG in all subjects, followed by a trajectory with a progressive return to baseline over a maximum of 12 weeks, although the minimum sEMG and minimum force were not always recorded at the same time point. Three participants were able to complete only one to two segments. Slope values of the sEMG-force relations were also found to vary across the different time segments. While sEMG-force slopes provide assessments of force generation capacity of the BT injected muscle, amplitude histograms from novel sEMG recordings during the voluntary tasks provide additional insights about differential actions of BT on the overall motor unit (MU) population over time. The results of our study indicate that there are potential short term as well as long term decrements in muscle control and activation properties after BT administration on the affected side of chronic stroke survivors. Muscle activation levels as recorded using sEMG, did not routinely return to baseline even at three months’ post injection. The concurrent clinical measures also did not follow the same time course, nor did they provide the same resolution as our experimental measures. It follows that even 12 weeks after intramuscular BT injections muscle recovery may not be complete, and may thereby contribute to pre-existing paresis.

a Dynamical Model of Muscle Activation, Fatigue and Recovery

Abstract: A dynamical model is presented as a framework for muscle activation, fatigue, and recovery. By describing the effects of muscle fatigue and recovery in terms of two phenomenological parameters (F, R), we develop a set of dynamical equations to describe the behavior of muscles as a group of motor units activated by voluntary effort. This model provides a macroscopic view for understanding biophysical mechanisms of voluntary drive, fatigue effect, and recovery in stimulating, limiting, and modulating the force output from muscles. The model is investigated under the condition in which brain effort is assumed to be constant. Experimental validation of the model is performed by fitting force data measured from healthy human subjects during a 3-min sustained maximal voluntary handgrip contraction. The experimental results confirm a theoretical inference from the model regarding the possibility of maximal muscle force production, and suggest that only 97% of the true maximal force can be reached under maximal voluntary effort, assuming that all motor units can be recruited voluntarily. The effects of different motor unit types, time-dependent brain effort, sources of artifacts, and other factors that could affect the model are discussed. The applications of the model are also discussed.

Robot technology

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Benchmarking CNC Machine Tool Using Hybrid-Fuzzy Methodology: A Multi-Indices Decision Making (MCDM) Approach

Abstract: In today’s era, managerial decision making has become a very momentous component due to the leverage of attention on achieving organizational goal i.e. enhancing effective utilization of input assets, satisfying customers’ demand and minimizing loss (maximize profit). The evaluation of the most appropriate Computer Numerical Control (CNC) machine tool has become one of the key factors for sustaining the organization/manufacturing sectors/production units at competitive global market place. Productivity, precision and accuracy etc. are the most important issues behind adaptation/exploration of CNC machine tools. So, in such a cases, subjective indices are considered beside the objective indices and complexity of the CNC machine tool evaluation decision problems is solved via subjective assessments (judgment) of expert panel, also called the decision-making group. In this reporting, TOPSIS (technique for order preference by similarity to ideal solution) based Multi-Criteria Decision Making (MCDM) approach has fruitfully applied to emphasize the decision making scenario at the subjective information evaluation index (indices) platform. So, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) analytical methodology conjunction with Trapezoidal Fuzzy Number (TFN) has been explored for assessing and benchmarking the most preferable CNC machine tool from a group of preferred options/alternatives. Finally, an empirical case study has been carried out check the feasibility, efficiency and validity of proposed methodology and the benchmarking of preferred alternative machine tool has been derived in accordance with descending value of the ‘collective index’. Higher value of ‘collective index’ reflects higher degree of performance extent. Benchmarking CNC Machine Tool Using HybridFuzzy Methodology: A Multi-Indices Decision Making (MCDM) Approach

Classification of Interbeat Interval Time-series Using Attention Entropy

Abstract: Classification of interbeat interval time-series which fluctuates in an irregular and complex manner is very challenging. Typically, entropy methods are employed to quantify the complexity of the time-series for classifying. Traditional entropy methods focus on the frequency distribution of all the observations in a time-series. This requires a relatively long time-series with at least a couple of thousands of data points, which limits their usages in practical applications. The methods are also sensitive to the parameter settings. In this paper, we propose a conceptually new approach called attention entropy, which pays attention only to the key observations. Instead of counting the frequency of all observations, it analyzes the frequency distribution of the intervals between the key observations in a time-series. The advantages of the attention entropy are that it does not need any parameter to tune, is robust to the time-series length, and requires only linear time to compute. Experiments show that it outperforms fourteen state-of-the-art entropy methods evaluated by real-world datasets. It achieves average classification accuracy of AUC=0.71 while the second-best method, multiscale entropy, achieves AUC=0.62 when classifying four groups of people with a time-series length of 100.