Classification of sporting activities using smartphone accelerometers

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach

Socialoscope: Sensing User Loneliness and Its Interactions with Personality Traits

Loneliness and social isolation can have a serious impact on one’s mental health, leading to increased stress, lower self-esteem, panic attacks, and drug or alcohol addictions. Older adults and international students are disproportionately affected by loneliness. This thesis investigates Socialoscope, a smartphone app that passively detects loneliness in smartphone users based on the user’s day-to-day social interactions, communication and smartphone activity sensed by the smartphone’s built-in sensors. Statistical analysis is used to determine smartphone features most correlated with loneliness. A previously established relationship between loneliness and personality type is explored. The most correlated features are used to synthesize machine learning classifiers that infer loneliness levels from smartphone sensor features with an accuracy of 90%. These classifiers can be used to make the Socialoscope an intelligent loneliness sensing Android app. The results show that, of the five Big-Five Personality Traits, emotional stability and extraversion personality traits are strongly correlated with the sensor features such as number of messages, number of outgoing calls, number of late night browser searches, number of long incoming or outgoing calls and number of auto-joined trusted Wi-Fi SSIDs. Moreover, the classifier accuracy while classifying loneliness levels is significantly improved to 98% by taking these personality traits into consideration. Socialoscope can be integrated into the healthcare system as an early warning indicator of patients requiring intervention or utilized for personal self-reflection

Managing the possible health risks of mobile telecommunications: Public understandings of precautionary action and advice

It has been suggested that precautionary approaches to managing possible health risks mobile telecommunications (MT) technology may cause or exacerbate public concerns. In contrast, precautionary approaches to managing such risks in the UK have been framed as a way of reducing public concerns. This article presents evidence from a series of focus groups about publics’ understandings of the actions taken and advice given about potential MT health risks by the UK government. Eight focus groups were conducted with members of the public that varied in their age, their awareness and concern about mast siting, and the self-reported level of mobile phone use. From the analyses a complex picture emerged in which publics’ understandings were not primarily framed in terms of precautionary action and advice either provoking concern or providing reassurance. People made sense of precaution by drawing upon a range of evidence from their understandings of costs and benefits of the technology, as well as the institutional context in which MT health risks were managed. For some of those involved in protesting against mast siting, precaution was seen as confirming existing concern. Further systematic exploration of the contexts within which different responses to precaution emerge is thus likely to be instructive.Mobile Telecommunications Health Research Programme

Distributed Online Machine Learning for Mobile Care Systems

Appendix D: Wavecomm Tech Docs removed for copyright reasonsTelecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis

A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

SMS, one of the most popular and fast-growing GSM value-added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F-measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine-tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state-of-the-art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F-measure.publishedVersio

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial