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A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition.

Sensors (Basel, Switzerland)
Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facil...

Self-debriefing Model Based on an Integrated Video-Capture System: An Efficient Solution to Skill Degradation.

Journal of surgical education
OBJECTIVE: Video-based teaching is considered highly effective in debriefing, especially in minimally invasive surgeries. In this study, the benefits of using a new integrated video recording system, were investigated and compared to those of the sta...

Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

The British journal of dermatology
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.

IEEE journal of biomedical and health informatics
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, whic...

Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone.

Sensors (Basel, Switzerland)
Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, espec...

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.

Current psychiatry reports
PURPOSE OF REVIEW: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been propo...

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applie...

m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

Methods (San Diego, Calif.)
Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare...

Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

Journal of diabetes science and technology
BACKGROUND: Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of resea...