Mining Personal Data Using Smartphones and Wearable Devices: A Survey

Rehman, Muhammad Habib ur; Liew, Chee Sun; Wah, Teh Ying; Shuja, Junaid; Daghighi, Babak

doi:10.3390/s150204430

Cited by 75 publications

(30 citation statements)

References 95 publications

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“…Rawassizadeh et al [35] leveraged machine algorithms to fetch semantic information from data. Moreover, Rehman et al [36] plotted the usefulness of machine learning methods for extracting abstract patterns from mobile phones and wearable devices. Therefore, for this work, multiple machine learning algorithms were developed and a comparative analysis was performed to determine the best possible algorithm based on the evaluation metrics.…”

Section: Machine Learning Algorithm and Evaluation Metricsmentioning

confidence: 99%

The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

et al. 2019

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The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios.

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Section: Machine Learning Algorithm and Evaluation Metricsmentioning

confidence: 99%

The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

et al. 2019

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“…The types of sensors usually included in wearables (mainly in wrist wearables) are key factors in order to develop a solution based on the results of our piece of research. We produced a list of available sensors in smartbands and smartwatches from the information available in recent publications (Swan, 2012) and (Rehman et al, 2015) and in several web portals. It is a complex task to find the real availability of each sensor type because the variety of devices and the continuous change.…”

Section: Study Of Wearablesmentioning

confidence: 99%

Analytics of biometric data from wearable devices to support teaching and learning activities

Arriba-Pérez

Santos-Gago

Caeiro-Rodríguez

2016

Journal of Information Systems Engineering & Management

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This paper introduces the preliminary results of a piece of research whose main purpose is to take advantage of data collected from wearable devices to support learning processes. This goal is approached through the application of learning analytic techniques. The innovation point is the use of data collected from wearables, that will be used in conjunction with data collected from other sources (e.g. Learning Management Systems, Student Information Systems). The paper reviews the results achieved during the last year about the relationships among biometric data collected from wearables and relevant features described in the educational literature. In this way sleep and stress have been identified as interesting areas that could be informed from data collected in wearables and processed by applying machine learning techniques. Our preliminary results show some initial promising results that need further validation, also these results show an interesting opportunity to support awareness and intervention functionalities.

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“…In the past five years, the focus shift to personalization increased the interest in considering the environment the patient lives in, also outside the hospital [60,61]. This has been done mainly by analyzing data coming from wearable sensors and mobile phones.…”

Section: Wearable Technologymentioning

confidence: 99%

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

Sacchi¹,

Holmes

2016

Yearb Med Inform

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Summary Objectives: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains.Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

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Mining Personal Data Using Smartphones and Wearable Devices: A Survey

Cited by 75 publications

References 95 publications

The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

Analytics of biometric data from wearable devices to support teaching and learning activities

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

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