Department of Computer Science and Information Systems

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Subject: search.filters.subject.Machine Learning

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Now showing 1 - 10 of 29
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    System Architecture of a Smart Fall Detection System
    (Springer, 2020-10) Ramachandran, Anita
    With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 years. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ~28 to ~50% in the European Union and from ~33 to ~45% in Asia [1]. Therefore, the percentage of people who need additional care is also expected to increase. Geriatric health care, which pertains to care for the elderly, has gained a lot of prominence in the recent years, with specific focus on fall and sleep apnea detection systems because of their impact on public lives. In the recent years, there has been widespread application of Internet of things (IoT) and machine learning in the geriatric healthcare domain because of the potential cost reduction such technologies can bring in. In this paper, we present the architecture and design of an end-to-end geriatric healthcare system, with focus on wearable device based fall detection using machine learning. We explain the major components of the system architecture, under a certain deployment scenario, and present the communication protocol between these system components. We also present the salient aspects of the multi-channel variable time-division multiple access (multi-channel V-TDMA) MAC protocol designed to suit the requirements of such a system. This protocol combines the strengths of both standard time-division multiple access (TDMA) that is modified to support flexibility and frequency-division multiple access (FDMA).
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    Performance Analysis of Machine Learning Algorithms for Sleep Apnea Detection Using ECG
    (Springer, 2021-10) Ramachandran, Anita
    Sleep apnea is a sleep disorder in which a sleeping person’s breathing is disturbed. Subjects suffering from sleep apnea undergo periods of no or shallow breathing during their sleep. Sleep apnea may lead to severe issues such as diabetes, cardiovascular problems, hypertension, neurological issues and liver problems. Because of the global prevalence of sleep apnea as well as the direct and indirect long-term problems it brings about, it is important to diagnose and treat this condition. Sleep apnea is detected clinically by the Polysomnography (PSG) test which measures various biomedical parameters such as electrocardiogram (ECG), electroencephalogram (EEG) and oxygen saturation (SpO2) over a full night’s sleep. The application of machine learning to detect sleep apnea from these parameters has gained ground in the recent past because of its ability to learn from the training datasets and generalize well to make predictions on new data. In this paper, we look at the performance of 6 machine learning classifiers—k-nearest neighbors (kNN), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest and XGBoost in their ability to detect apneic events. The study is based on datasets with ECG signals.
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    Performance Analysis of Machine Learning Algorithms for Fall Detection
    (IEEE, 2019) Ramachandran, Anita
    Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. Application of machine learning in areas of AALS such as fall detection has the potential to have huge social impact. There has been active research in the application of machine learning in fall detection, using data generated by various means such as wearable devices, environment sensors and vision based systems. The main challenge is to create a model that detects falls accurately, while keeping the design of the fall detection system minimal and non-intrusive. Wearable devices equipped with inertial motion unit (IMU) sensors and vital signs sensors are commonly used to enable analysis around performance of machine learning (ML) models. In this paper, we analyze the impact of using IMU sensor parameters in combination with vital signs parameters, on the performance of ML algorithms for fall detection. We present details on the data set we have generated for this purpose, and compare the performance of various ML algorithms on the collected dataset, with features from IMU sensors vis-à-vis those from IMU sensors in combination with vital signs sensors. We also apply machine learning algorithms on two public datasets, one with only IMU sensor parameter values and the second with only vital signs parameter values, and summarize their performance.
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    Machine learning algorithms for fall detection using kinematic and heart rate parameters-a comprehensive analysis
    (International Journal of Artificial Intelligence, 2020) Ramachandran, Anita
    The application of machine learning techniques to detect and classify falls is a prominent area of research in the domain of intelligent assisted living systems. Machine learning (ML) based solutions for fall detection systems built on wearable devices use various sources of information such inertial motion units (IMU), vital signs, acoustic or channel state information parameters. Most existing research rely on only one of these sources; however, a need to do more experimenation to observe the efficiency of the ML classifiers while coupling features from diverse sources, was felt. In addition, fall detection systems based on wearable devices, require intelligent feature engineering and selection for dimensionality reduction, so as to reduce the computational complexity of the devices. In this paper we do a comprehensive performance analysis of ML classifiers for fall detection, on a dataset we collected. The analysis includes the impact of the following aspects on the performance of ML classifiers for fall detection: (i) using a combination of features from 2 sensors-an IMU sensor and a heart rate sensor, (ii) feature engineering and feature selection based on statistical methods, and (iii) using ensemble techniques for fall detection. We find that the inclusion of heart rate along with IMU sensor parameters improves the accuracy of fall detection. The conclusions from our experimentations on feature selection and ensemble analysis can serve as inputs for researchers designing wearable device-based fall detection systems
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    Machine Learning-based Fall Detection in Geriatric Healthcare Systems
    (IEEE, 2018) Ramachandran, Anita
    Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. According to the studies conducted by the Govt. of India, elderly population in India has reached 8.3% of the total population [40]. Per the National Program for Health Care of the Elderly (NPHCE), the elderly population in India has tripled over the last 50 years, and is projected to increase to 33.32 million by 2021 and 300.96 million by 2051 [41]. Application of machine learning in AALS, such as fall detection, therefore, has the potential to have huge public impact. In this paper, we propose a fall detection system that takes into account not only various wearable sensor node parameter readings for a subject, but also his biological and physiological profile. The profile is used to determine a fall risk category for the subject. We performed machine learning experiments using public datasets for fall detection which included wearable sensor node readings. The algorithms were then retrained by feeding in the risk categorization of the subject, and results from this analyses are presented. The objective of the experiments was to find out the impact of a subject's risk categorization on the accuracy of fall detection. The algorithms presented here form part of a comprehensive geriatric healthcare system under development, which comprises wearable sensor nodes, coordinator nodes, an indoor localization framework and cloud-hosted application servers. A brief overview of the system capabilities is also presented.
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    Evaluation of Feature Engineering on Wearable Sensor-based Fall Detection
    (IEEE, 2020) Ramachandran, Anita
    Internet of Things (IoT) enabled geriatric healthcare systems have gained importance in the recent years due to an increase in the number of elderly people living alone. The application of machine learning (ML) in areas of geriatric healthcare such as fall detection, has, consequently been an area of active research. Wearable systems for fall detection has the advantage of being light-weight and low power-consuming, yet reasonably accurate without being overly intrusive. However, the accuracy of fall detection using wearable systems depends, among other factors, on the types of sensors embedded in them. The use of inertial motion unit (IMU) sensors for fall detection, combined with machine learning classifiers applied on datasets collected from IMU sensors is an area of active research. In this paper, we analyze the impact of using IMU sensor parameters in combination with vital signs parameters, on the performance of ML algorithms for fall detection. We compare the performance of various ML algorithms on the dataset we collected for this purpose. We also perform statistical analysis to examine the relative importance of the various features on the behavior of ML classifiers.
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    Machine Learning-Based Techniques for Fall Detection in Geriatric Healthcare Systems
    (IEEE, 2018) Ramachandran, Anita
    Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. According to the studies conducted by National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%-10% requiring utmost care. Application of machine learning in areas of AALS such as fall detection, therefore, has the potential to have huge public impact. In this paper, we propose a fall detection system that takes into account not only various wearable sensor node parameter readings for a subject, but also his biological and physiological profile. The profile is used to determine a fall risk category for the subject. We performed machine learning experiments using public datasets for fall detection which included wearable sensor node readings. The algorithms were then retrained by feeding in the risk categorization of the subject, and results from this analyses are presented. The objective of the experiments was to find out the impact of a subject's risk categorization on the accuracy of fall detection. The algorithms presented here form part of a comprehensive geriatric healthcare system under development, which comprises wearable sensor nodes, co-ordinator nodes, an indoor localization framework and cloud-hosted application servers. A brief overview of the system capabilities is also presented.
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    A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems
    (MDPI, 2021) Ramachandran, Anita
    Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.
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    Motivation of participants on the crowdsourcing platform using intelligent agents
    (Research Institute for Intelligent Computer Systems, 2020-03) Anand, Vijayalakshmi
    Crowdsourcing is a model where individuals or organizations receive services from a large group of Internet users including ideas, finances, completing a complex task, etc. Several crowdsourcing websites have failed due to la ck of user participation; hence, the success of crowdsourcing platforms is manifested by the mass of user participation. However, an issue of motivating users to participate in crowdsourcing platform stays challenging. We have proposed a new approach, i.e. , reinforcement learning - based gamification method to motivate users. Gamification has been a practical approach to engaging users in many fields, but still, it needs an improvement in the Crowdsourcing platform. In this paper, the gamification approach is strengthened by a reinforcement learning algorithm. We have created an intelligent agent using the Reinforcement learning algorithm (Q - learning). This agent suggests an optimal action plan that yields maximum reward points to the users for their active pa rticipation in the Crowdsourcing application. Also, its performance is compared with the SARSA algorithm (On - policy learning), which is another Reinforcement learning algorithm.
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    Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
    (ARXIV, 2022-10) Agarwal, Vinti
    Due to high mutation rates, COVID-19 evolved rapidly, and several variants such as Alpha, Gamma, Delta, Beta, and Omicron emerged with altered viral properties like the severity of the disease caused, transmission rates, etc. These variants burdened the medical systems worldwide and created a massive impact on the world economy as each had to be studied and dealt with in its specific ways. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and then compares the results from different dimensionality reduction methods including: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation Projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for a particular variant (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We conclude that the proposed framework can effectively distinguish between the major variants and hence can be used for the identification of emerging variants in the future.