Prediction of nocturia in live alone elderly using unobtrusive in-home sensors

iCity Lab; SHINESeniors; National Research Foundation (NRF) Singapore under the Land and Livability National Innovation Challenge (L2NIC

Unsupervised machine learning for developing personalised behaviour models using activity data

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner

Central monitoring system for ambient assisted living

Smart homes for aged care enable the elderly to stay in their own homes longer. By means of various types of ambient and wearable sensors information is gathered on people living in smart homes for aged care. This information is then processed to determine the activities of daily living (ADL) and provide vital information to carers. Many examples of smart homes for aged care can be found in literature, however, little or no evidence can be found with respect to interoperability of various sensors and devices along with associated functions. One key element with respect to interoperability is the central monitoring system in a smart home. This thesis analyses and presents key functions and requirements of a central monitoring system. The outcomes of this thesis may benefit developers of smart homes for aged care

An Examination of Negative Correlations Using Pearson Correlation Analysis to Optimize the Diversification of Cryptocurrency Portfolios

The purpose of this study is to employ the Pearson correlation approach in order to assess the association between different types of cryptocurrencies. The dataset included in this research comprises daily peak price information for 10 distinct categories of cryptocurrencies with the biggest market capitalizations from October 1, 2017 to December 31, 2022. Assessing and computing the correlation between cryptocurrency pairs with the Pearson correlation coefficient is the objective. The information utilized in this study was acquired from the website www.coinmarketcap.com. Pairs of stablecoins and crypto coin assets have the largest negative correlation, according to the findings of this study, in contrast to pairs of crypto currency assets. The pair ETH-BNB has the strongest positive correlation with a value of 0.948, while the pair LTC-USDT has the most negative correlation at -0.347. In order to replicate the impact of the negative correlation on trading activities, an exchange simulation was performed between the LTC and USDT pairings. Based on the outcomes of the simulation, the asset rise resulting from the exchange of the LTC and USDT pair from January 1, 2022 to December 31, 2022 was 12.09 percent. During the same time period, the asset's value would have declined by -48.69 percent if LTC was held. Conversely, an expansion of the time period from October 1, 2017 to December 31, 2022 yields an asset gain of 251,047.85 percent as a consequence of the exchange between LTC and USDT. Those individuals interested in reducing risk and diversifying their portfolios with cryptocurrency investments may find this information highly beneficial. The results of this research offer significant contributions to the current body of literature on bitcoin investment and offer investors valuable informatio

Empirical Studies for Reliable Home Area Wireless Sensor Networks

Home Area Networks: HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy and assisted living. A key challenge faced by HANs is maintaining reliable operation in real-world residential environments. In this thesis research, empirical studies on the spectrum usage in the 2.4 GHz band as well as 802.15.4 wireless channels are performed in diversified real residential environments. Based on the insights drawn from empirical studies, network design guideline and practical solution for Home Area Sensor Network are provided

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Parkinson’s Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson\u27s disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson’s Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers’ performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson’s disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD