An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Objective measures, sensors and computational techniques for stress recognition and classification

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

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Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Activity detection and classification are very important for autonomous monitoring of humans for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors have found wide-spread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, such as accelerometer, gyroscope, and camera, it has become more feasible to develop activity monitoring algorithms employing one or more of these sensors with increased accessibility. We provide a complete and comprehensive survey on activity classification with wearable sensors, covering a variety of sensing modalities, including accelerometer, gyroscope, pressure sensors, and camera- and depth-based systems. We discuss differences in activity types tackled by this breadth of sensing modalities. For example, accelerometer, gyroscope, and magnetometer systems have a history of addressing whole body motion or global type activities, whereas camera systems provide the context necessary to classify local interactions, or interactions of individuals with objects. We also found that these single sensing modalities laid the foundation for hybrid works that tackle a mix of global and local interaction-type activities. In addition to the type of sensors and type of activities classified, we provide details on each wearable system that include on-body sensor location, employed learning approach, and extent of experimental setup. We further discuss where the processing is performed, i.e., local versus remote processing, for different systems. This is one of the first surveys to provide such breadth of coverage across different wearable sensor systems for activity classification.

A Survey on Activity Detection and Classification Using Wearable Sensors

Acute mental stress assessment via short term HRV analysis in healthy adults : a systematic review with meta-analysis

The work illustrated in this paper seeks to establish the case around children suffering from autism in developing countries. This paper proposes the design and development of a ubiquitous computing framework (codenamed Project Communicate) to provide a playful HCI model in order to help them learn and communicate effectively. The key merit of this study is in the illustration of how economical off-the-shelf technologies can be effectively integrated to achieve two key use cases in the area of autism interventions, namely Communication and Pedagogy. The proposed framework congregates some of the interventions designed and implemented by the authors in each of these test cases with a special focus on how mobile phone usage could be operationalized and leveraged in the context of a developing country like India.

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Empowering Children with Autism and Their Caregivers in India

AWS elastic cache (Redis) is one the most popular mechanism for cloud developers to achieve both excellent caching as well as persistency of data. As part of our recent project of building personalised ad decision system, we needed to support stringent performance requirement of 125000 requests per second and 95% of requests must be served under 250ms. We used Elastic cache (Redis) for both caching and persistency and found number of optimisation techniques which helped us to achieve the performance requirement. These techniques we used ensured that cost for Elastic cache (and the whole project) is much lower than initial estimate. The techniques involved choosing appropriate CPU instances for Elastic cache, using right cache structure and whole host of changes on Redis client side to make this end-to-end system high performance, and cost effective.

Rahul Banerjee

Papers

Empowering Children with Autism and Their Caregivers in India

Optimisations on AWS Elastic Cache (Redis) Usage

Clinical Validation of a Continuous Monitoring Mobile Cardiac Detection Device for Atrial Fibrillation.