Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> The article describes an active map service (AMS) that supports context-aware computing by providing clients with information about located-objects and how those objects change over time. The authors focus on the communication issues of disseminating information from an active map server to its clients, and in particular, address how to deal with various overload situations that can occur. Simple unicast callbacks to interested clients work well enough if only a few located-objects are moving at any given time and only a few clients wish to know about any given move. However, if many people are moving about in the same region and many clients are interested in their motion, then the AMS may experience overload due to the quadratic nature of the communications involved. This overload affects both the server as well as any slow communications links being used. Mobile distributed computing enables users to interact with many different mobile and stationary computers over the course of the day. Navigating a mobile environment can be aided by active maps that describe the location and characteristics of objects within some region as they change over time. > <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> We are developing personal health monitors based on a wireless body area network (BAN) of intelligent sensors. Individual monitors will be integrated into a distributed wireless system for synchronized monitoring of a group of subjects. This system could be used during the selection process and as part of a psychophysiological evaluation of military members undergoing intense training. We use measures of heart-rate variability to quantify stress level prior to and during training as well as to predict stress resistance. This task requires reliable, high-precision instrumentation and synchronized measurements from a group of individuals over prolonged periods (days of training). <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> The present study discusses the construction of a ubiquitous health care system that can cope actively with urgent situations of chronic disease patients who can lead everyday life despite their chronic diseases. If a patient's emergency is detected by personal health care host (PHCH), which is composed of USN, electronic stethoscope, GPS, CDMA and RFID, a text message containing position information is sent immediately to the hospital and the guardian's mobile terminal so that they can take an action immediately. In particular, data from accelerometers and vibration sensors, which are composed of ubiquitous sensors, are learned using the back-propagation network algorithm, therefore emergencies such as syncope and convulsion are determined actively. The experiments show that this system is very effective to find emergencies promptly for chronic disease patients who cannot take care of themselves and it is expected to save many lives. <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity. <s> BIB004 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> that paired six professional older adult actors with six older adults with moderate-to-severe AD. The actors trained on video footage of people with AD engaging in the activity of handwashing. The actors completed handwashing for two conditions: 1) 20 trials with assistance from a human caregiver; and 2) 20 trials with assistance from an automated guidance system, specifically the COACH prompting system. Video clips of both the actors and people with AD performing handwashing with caregiver assistance were randomly shown to 11 professional caregivers, who were asked to rate their believability. Results: Statistical analysis did not find a significant difference between the believability of the actor or person with AD groups, thus indicating that the actors were as believable as the actual older adults with AD. In addition, analysis of the trials where the automated guidance system was used showed similar reactions to the technology from both groups. Conclusions: This pilot work suggests that using professional older adult actors to optimize assistive technologies for older adults with dementia may constitute a viable alternative to clinical trials alone. If so, this approach could result in significant savings in terms of development time, costs, and demands on the clinical population. <s> BIB005 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> The use of sensor networks for healthcare, well-being, and working in extreme environments has long roots in the engineering sector in medicine and biology community. With the growing needs in ubiquitous communications and recent advances in very-low-power wireless technologies, there has been considerable interest in the development and application of wireless networks around humans. With the maturity of wireless sensor networks, body area networks (BANs), and wireless BANs (WBANs), recent efforts in promoting the concept of body sensor networks (BSNs) aim to move beyond sensor connectivity to adopt a system-level approach to address issues related to biosensor design, interfacing, and embodiment, as well as ultra low-power processing / communication, power scavenging, autonomic sensing, data mining, inferencing, and integrated wireless sensor microsystems. As a result, the system architecture based on WBAN and BSN is becoming a widely accepted method of organization for ambulatory and ubiquitous monitoring systems. This review paper presents an up-to-date report of the current research and enabling applications and addresses some of the challenges and implementation issues. <s> BIB006 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements. <s> BIB007 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> Smart home is regarded as an independent healthy living for elderly person. Advances in phone technology and new style of computing paradigm (i.e., cloud computing) permits real time acquisition, processing, and tracking of activities in smart home. In this paper, we develop android smartphone application to assists elderly people for independent living in their own homes. It reduces the health expenditures and burden of health care professionals in care facility units. We assume smart home as an intelligent agent to perceive the environment and process the sensory data on cloud. Smartphone application communicates with cloud through web services and assists the elderly person to complete their daily life activities. It facilitates the care giver assistant by tracking the elderly persons in their own homes and avoids certain accidents. Furthermore, it also helps the family members to track the activities, when they are outside from homes. <s> BIB008 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches. <s> BIB009 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN. <s> BIB010 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> PERVASIVE SENSING AND M-HEALTH: VITAL SIGNS AND DAILY ACTIVITY MONITORING .- ARE TECHNOLOGIES ASSISTED HOMES SAFER FOR THE ELDERLY? .- A LARGE-AREA SENSOR SYSTEM UNDERNEATH THE FLOOR FOR AMBIENT ASSISTED LIVING APPLICATIONS .- FOOTWEAR-BASED WEARABLE SENSORS FOR PHYSICAL ACTIVITY MONITORING98 .- CONTINUOUS-WAVE PHOTOACOUSTIC-BASED SENSOR FOR THE DETECTION OF AQUEOUS GLUCOSE: TOWARDS NON-INVASIVE AND CONTINUOUS GLYCEMIA SENSING .- FROM HANDHELD DEVICES TO NEAR-INVISIBLE SENSORS: THE ROAD TO PERVASIVE E-HEALTH .- A UNIVERSAL WIRELESS DEVICE FOR BIOMEDICAL SIGNALS RECORDING .- WIRELESS SENSING SYSTEM FOR HEALTHCARE MONITORING PHYSIOLOGICAL STATE AND RECOGNIZING BEHAVIOR IN DAILY LIFE .- AUTOMATIC SENSING OF SPEECH ACTIVITY AND CORRELATION WITH MOOD CHANGES .- THE POTENTIAL OF PERVASIVE SENSORS AND COMPUTING FOR POSITIVE TECHNOLOGY: THE INTERREALITY PARADIGM .- UTILIZING SOCIAL MEDIA, MOBILE DEVICES AND SENSORS FOR CONSUMER HEALTH COMMUNICATION: A FRAMEWORK FOR CATEGORIZING EMERGING TECHNOLOGIES AND TECHNIQUES .- EHR ECOSYSTEM .- ACQUISITION AND ANALYSIS OF BIOMEDICAL SIGNALS IN CASE OF PEOPLES EXPOSED TO ELECTROMAGNETIC FIELDS .- MODELING DEPENDABILITY OF IT SERVICES ASSOCIATED WITH SOCIAL AND ECONOMIC INFRASTRUCTURE INCLUDING HEALTHCARE .- REQUIREMENTS AND BARRIERS TO PERVASIVE HEALTH ADOPTION . <s> BIB011 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Healthcare and Well-Being <s> The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize the human affective state given four physiological signals. We describe difficult issues unique to obtaining reliable affective data and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-to-day variations: The features of different emotions on the same day tend to cluster more tightly than do the features of the same emotion on different days. To handle the daily variations, we propose new features and algorithms and compare their performance. We find that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projection and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral. <s> BIB012
Previously, the use of mobile devices within the context of the ubiquitous sensing has been successfully integrated in zoology and veterinary medicine to study the feeding habits and social behaviors of some types of animals from zebra to whales; whereas, the adaptation of this technology to human health has been paid attention recently. With the advancements and increasing deployment of microsensors and low-power wireless communication technologies within the Personal/Body Area Network (PAN/BAN), the BIB001 Electrocardiography (ECG), Blood volume pulse (BVP), Galvanic skin response (GSR) and Electromyography (EMG). studies conducted under ubiquitous computing have grown interest in healthcare domain. Besides high demands for applying and understanding Human Activity Recognition (HAR) based systems, the integration of monitoring and analyzing vital sign data (e.g., heart rate, blood sugar level and pressure level, respiration rate, skin temperature, etc.) through sensors also more likely enable to change assessment, treatment and diagnostic methodologies in healthcare domain since traditional methodologies have been based on self-reports, clinic visits and regular doctor inspections BIB011 . With the integration of emerging technologies in healthcare domain, sensor-enabled autonomous mobile devices can help caretakers continuously monitor patients, record their wellbeing process, and report any acute situation in case where abnormal behavior is detected. Thereby, it would be more easier and efficient to monitor and manage the lifestyles and wellbeings of patients with chronic diseases, the elderly people, the rehab taking patients, the patients dealing with obesity, the patients with cognitive disorders, children, and even more significantly to monitor and rescue the emergent vitals and status notifying soldiers in combat zone. The home-based health care monitoring by mobile based devices is defined under smart home applications. The studies in BIB005 - BIB008 are carried out in order to create a smart home environment for treatment procedures of patients (e.g., having cardiac problem BIB009 , or diabetics) based on collecting data through different wearable physiological sensors (e.g., body temperature, heart rate, blood pressure, blood oxygen values, respiration level, and ECGs) and also reporting feedbacks remotely to the healthcare givers. The wearable sensors (including accelerometers, heart rate monitors and many others) have been also studied in BIB006 - BIB007 to recognize activity patterns for measuring fitness level, and discovering frequentness of body movement against obesity and weight loss programs BIB010 , diagnosing insidious diseases BIB003 (e.g., hypotension), and understanding emotional states BIB012 , BIB002 (e.g., stress level). Besides, smartphones can be used as a reminding systems BIB005 for aging related cognitive disorders such as Alzheimer treatment. Also, like a study called UbiFit in BIB004 , smartphone can capture user relevant physical activity level and corresponds the obtained information to personal fitness goals by presenting feedback reports back to the user. There are many commercial products available at the market to give ubiquitous computing solutions in the healthcare domain. These products are mostly concentrated on assisting people on controlling dietary programs/weight management, discovering fitness level, measuring burnt calorie or energy level, counting step numbers, and recognizing activities. Philips Directlife, FitBit Zip and BodyMedia GoWear are some device examples produced for tracking activity patterns, counting steps, measuring calorie burnt, and calculating distance traveled. In addition, Impact Sports ePulse proposes heart pulse monitoring system. Many other products can also be found for measuring heat flux, galvanic skin response and skin temperature.