Kunal Mankodiya - Academia.edu (original) (raw)

Papers by Kunal Mankodiya

Behavioural Brain Research, Apr 1, 2021

The cortical role of the motor symptoms reflected by kinematic characteristics in Parkinson's... more The cortical role of the motor symptoms reflected by kinematic characteristics in Parkinson's disease (PD) is poorly understood. In this study, we aim to explore how PD affects cortico-kinematic interactions. Electroencephalographic (EEG) and kinematic data were recorded from seven healthy participants and eight participants diagnosed with PD during a set of self-paced finger tapping tasks. Event-related desynchronization (ERD) was compared between groups in the α (8-14 Hz), low-ß (14-20 Hz), and high-ß (20-35 Hz) frequency bands to investigate between-group differences in the cortical activities associated with movement. Average kinematic peak amplitudes and latencies were extracted alongside Sample Entropy (SaEn), a measure of signal complexity, as variables for comparison between groups. These variables were further correlated with average EEG power in each frequency band to establish within-group interactions between cortical motor functions and kinematic motor output. High ß-band power correlated with mean kinematic peak latency and signal complexity in the healthy group, while no correlation was found in the PD group. Also, the healthy group demonstrated stronger ERD in the broad ß-band than the PD participants. Our results suggest that cortical ß-band power in healthy populations is graded to finger tapping latency and complexity of movement, but this relationship is impaired in PD. These insights could help further enhance our understanding of the role of cortical ß-band oscillations in healthy movement and the possible disruption of that relationship in PD. These outcomes can provide further directions for treatment and therapeutic applications and potentially establish cortical biomarkers of Parkinson's disease.

Communication based on brain-computer interface (BCI) systems is still a challenge. Although most... more Communication based on brain-computer interface (BCI) systems is still a challenge. Although most popular classes of BCIs heavily rely on electroencephalography (EEG), recent studies have demonstrated the feasibility of using functional near-infrared spectroscopy (fNIRS) as a reliable control signal in BCI systems. However, due to the inherent latency in hemodynamic responses, these systems are considerably slow. To address this issue, this study proposes an innovative oddball-based visio-mental task and investigates the feasibility of developing an fNIRS speller. The proposed paradigm derives its principles from the conventional oddball paradigm, which has been modified to include a set of mental arithmetic operations in the "flash" condition. Using statistical parametric mapping (SPM) and Pearson correlation analysis, the optimum channels and hemodynamic features were selected respectively. Linear discriminant analysis (LDA) was used to evaluate the performance of the proposed fNIRS-speller. Using 2 optimum channels, our analysis demonstrated the highest average accuracy of 78.5 ± 5.7% within 2-4 seconds of the stimulation and an average accuracy of 77.0 ± 8.9% only within the first 2 seconds. Achieving satisfactory performance while using only 2 channels and a 2-second window highlights the feasibility of developing a convenient and real-time fNIRS-speller. Such a system may have potential translational applications, particularly in users with a lack of eye gaze control.

IGI Global eBooks, 2019

Big data analytics with the cloud computing are one of the emerging area for processing and analy... more Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.

Advances in hospitality, tourism and the services industry (AHTSI) book series, 2018

This chapter proposes and develops a cloud-computing-based SDI model named as TCloud for sharing,... more This chapter proposes and develops a cloud-computing-based SDI model named as TCloud for sharing, analysis, and processing of spatial data particularly in the Temple City of India, Bhubaneswar. The main purpose of TCloud is to integrate all the spatial information such as tourism sites which include various temples, mosques, churches, monuments, lakes, mountains, rivers, forests, etc. TCloud can help the decision maker or planner or common users to get enough information for their further research and studies. It has used open source GIS quantum GIS for the development of spatial database whereas QGIS plugin has been linked with quantum GIS for invoking cloud computing environment. It has also discussed the various spatial overlay analysis in TCloud environment.

Biosensors

Biopotential electrodes play an integral role within smart wearables and clothing in capturing vi... more Biopotential electrodes play an integral role within smart wearables and clothing in capturing vital signals like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). This study focuses on dry e-textile electrodes (E1–E6) and a laser-cut knit electrode (E7), to assess their impedance characteristics under varying contact forces and moisture conditions. Synthetic perspiration was applied using a moisture management tester and impedance was measured before and after exposure, followed by a 24 h controlled drying period. Concurrently, the signal-to-noise ratio (SNR) of the dry electrode was evaluated during ECG data collection on a healthy participant. Our findings revealed that, prior to moisture exposure, the impedance of electrodes E7, E5, and E2 was below 200 ohm, dropping to below 120 ohm post-exposure. Embroidered electrodes E6 and E4 exhibited an over 25% decrease in mean impedance after moisture exposure, indicating the impact of stitch design and mois...

Frontiers in Sensors

Sensor networks and IoT systems have been widely deployed in monitoring and controlling system. W... more Sensor networks and IoT systems have been widely deployed in monitoring and controlling system. With its increasing utilization, the functionality and performance of sensor networks and their applications are not the only design aims; security issues in sensor networks attract more and more attentions. Security threats in sensor and its networks could be originated from various sectors: users in cyber space, security-weak protocols, obsolete network infrastructure, low-end physical devices, and global supply chain. In this work, we take one of the emerging applications, advanced manufacturing, as an example to analyze the security challenges in the sensor network. Presentable attacks—hardware Trojan attack, man-in-the-middle attack, jamming attack and replay attack—are examined in the context of sensing nodes deployed in a long-range wide-area network (LoRaWAN) for advanced manufacturing. Moreover, we analyze the challenges of detecting those attacks.

Frontiers in Human Neuroscience

Near-infrared spectroscopy (NIRS) is a promising research tool that found its way into the field ... more Near-infrared spectroscopy (NIRS) is a promising research tool that found its way into the field of brain-computer interfacing (BCI). BCI is crucially dependent on maximized usability thus demanding lightweight, compact, and low-cost hardware. We designed, built, and validated a hybrid BCI system incorporating one optical and two electrical modalities ameliorating usability issues. The novel hardware consisted of a NIRS device integrated with an electroencephalography (EEG) system that used two different types of electrodes: Regular gelled gold disk electrodes and tri-polar concentric ring electrodes (TCRE). BCI experiments with 16 volunteers implemented a two-dimensional motor imagery paradigm in off- and online sessions. Various non-canonical signal processing methods were used to extract and classify useful features from EEG, tEEG (EEG through TCRE electrodes), and NIRS. Our analysis demonstrated evidence of improvement in classification accuracy when using the TCRE electrodes co...

UNSTRUCTURED Recent growth of electronic health (e-health) is unprecedented, especially after the... more UNSTRUCTURED Recent growth of electronic health (e-health) is unprecedented, especially after the COVID-19 pandemic. Within e-health, wearable technology is increasingly adopted since it can offer the remote monitoring of chronic and acute conditions in daily life environments. Wearable technology may be used to monitor and track key indicators of physical and psychological stress in daily life settings, providing helpful information for clinicians. One of the key challenges is to present the extensive wearable data to clinicians in an easily interpretable way for making informed decisions. The purpose of the presented research was to design a webapp dashboard, named CarePortal, for analytic visualizations of wearable data that are meaningful to clinicians. The study was divided into two main research objectives (ROs): (RO1) Understand the needs of clinicians regarding wearable data interpretation and visualization. (RO2) Develop a system architecture of a web app to visualize weara...

2022 IEEE MIT Undergraduate Research Technology Conference (URTC)

IEEE Access

This is a literature review paper covering state-of-the-art sleep technologies to measure sleep a... more This is a literature review paper covering state-of-the-art sleep technologies to measure sleep and clinical sleep disorders. This paper addresses an interdisciplinary audience from a variety of subdomains in engineering and medicine. We reviewed 120 scientific papers, 15 commercial mobile apps, and 4 commercial devices. We selected the papers from scientific publishers including Institute of Electrical and Electronics Engineers (IEEE), Nature, Association for Computing Machinery (ACM), Proceedings of Machine Learning Research, Journal of Informatics in Health and Biomedicine, Plos One, PubMed, and Elsevier and Nature digital libraries. We used Google Scholar with keywords including ''sleep monitoring'', ''sleep monitoring technologies'', ''non-contact sleep monitoring'', ''mobile apps for sleep monitoring'', ''AI in sleep technologies'', and ''automated sleep staging.'' The manuscript reviews sleep technologies, including sleep lab technologies such as polysomnography and consumer sleep technologies categorized as ambient room sensors, wearable sensors, bed sensors, mobile apps, and artificial intelligence. We primarily focused on validation and comparison studies of the reviewed technologies. The manuscript also provides an overview of several clinical datasets for sleep staging and taxonomizes the different learning methods. Finally, the manuscript offers our insights and recommendations about the application of the reviewed sleep technologies.

Pervasive and Mobile Computing

2019 IEEE MIT Undergraduate Research Technology Conference (URTC)

Parkinsons disease has no known cure, but there do exist treatment plans which help those affecte... more Parkinsons disease has no known cure, but there do exist treatment plans which help those affected to slow the progression of the disease. These treatments are determined by the doctor's subjective observations of the patient during clinical visits. However, these visits are limited to just a few times a year. This paper attempts to migrate the subjective observations to objective measurements tested through the use of a smart glove and an Android application. In order to validate this concept, the exercises are first tried with healthy participants. We recruited 10 people to perform six hand exercises as specified by the Unified Parkinsons Disease Rating Scale (UPDRS). Our goal is to classify each of these six hand exercises using traditional machine learning techniques like K-nearest neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), Decision Tree (DT), Support Vector Machines (SVM). We achieved 90% accuracy in the classification of the six exercises.

2021 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

Sleep apnea is a prevalent and life-threatening problem, especially in infants. Napnea is a cost-... more Sleep apnea is a prevalent and life-threatening problem, especially in infants. Napnea is a cost-effective neonatal (sleep) apnea detection system that aims to provide affordable alternative methods for continuous respiration monitoring and apnea detection. For infants diagnosed with sleep apnea, the current monitoring systems rely on sticky electrodes wired to a cardio-respiratory monitor or expensive smart devices. Napnea is a compact and affordable solution for apnea monitoring utilizing a soft, smart e-textile chest belt, integrated with a smartphone app.

Near Field Communication (NFC) is a perfect example of ubiquitous computing that is secured, shor... more Near Field Communication (NFC) is a perfect example of ubiquitous computing that is secured, short-ranged, low-powered contactless communication. High demand is predicted for NFC, especially with wearables, and the variety of applications may require that this technology be fabricated onto different materials. In this research, we first designed flexible NFC antennas based on mathematical models. A simulation of the NFC coil inductance was performed and verified using the predictive models. Later, two flexible NFC antennas, including 160x80 mm 2 (rectangular) and 80x80 mm 2 (square) were fabricated through a chemical etching process and verified at 13.56 MHz frequency. Two experiments 1) Read range detection and 2) User experience study on 22 participants validate longer read range and shorter connection time of our fabricated flexible NFC tags.