Department of Computer Science and Information Systems
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Item Performance Analysis of Machine Learning Algorithms for Sleep Apnea Detection Using ECG(Springer, 2021-10) Ramachandran, AnitaSleep 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.Item Performance Analysis of Machine Learning Algorithms for Fall Detection(IEEE, 2019) Ramachandran, AnitaIntelligent 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.Item Machine Learning-based Fall Detection in Geriatric Healthcare Systems(IEEE, 2018) Ramachandran, AnitaIntelligent 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.Item Evaluation of Feature Engineering on Wearable Sensor-based Fall Detection(IEEE, 2020) Ramachandran, AnitaInternet 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.Item Machine Learning-Based Techniques for Fall Detection in Geriatric Healthcare Systems(IEEE, 2018) Ramachandran, AnitaIntelligent 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.Item A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems(MDPI, 2021) Ramachandran, AnitaSleep 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.