Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Mobile Sensor Data Measurements and Analysis for Fall Detection in Elderly Health Care

In recent years, increased life expectancy in Finland and other parts of the world have led to an aging population. Accidental falls can cause severe injuries to elderly people, thereby, negatively impacting their quality of life and in some cases resulting in death. Accidental falls is a major public health care challenge. Real time monitoring of human activity can provide insight into an individual’s functional ability and gives an indication of their ability to live independently. Automatic detection of falls enables us to provide timely medical attention, thereby, reducing the negative consequences of falls. This paradigm of home based health promotes independent living and reduces the burden on caregivers. The aim of the thesis is to log real world sensory data from multiple sensors on board mobile devices and develop suitable algorithms to extract information from the data to solve the problem of detecting when elderly people fall down. In order to log the data, an Android application is developed that collects data from the various onboard sensors and stores it in a text file. The developed application is used to take measurements of sensor data pertaining to various human activities. Then patterns in the data are then analysed and exploited to distinguish between normal day-to-day activities and people falling down. To detect falls, we develop two algorithms based on statistical detection theory and convex optimization, respectively and also analyze the efficacy of these methods

Actividad física laboral y composición corporal en mujeres adultas

Introducción: Las actividades durante la jornada laboral, pueden diferenciarse entre sí por su gasto energético y algunos de ellos podrían beneficiar la salud de acuerdo a sus características. Objetivo: Analizar y comparar la composición corporal y las características de la actividad física, a través de la acelerometría en las jornadas laborales de las trabajadoras administrativas y trabajadoras auxiliares de aseo de la Universidad Viña del Mar. Métodos: Se realiza un registro en jornadas de 10 hrs. por cuatro días seguidos del gasto energético a través de acelerómetros triaxiales a 8 secretarias y 8 auxiliares de aseo. Además se hace una evaluación antropométrica y se aplica el IPAQ (International Physical Activity Questionnaire). Resultados: Según el IPAQ, ambos grupos se encuentran en categoría de sedentarias, pero la acelerometría determina que las auxiliares caminan más pasos, tienen más quiebres sedentarios y realizan un nivel de actividad física más alto que las secretarias. Discusión: Hay trabajos que pueden favorecer el estado de salud, a pesar de no cumplir con la norma para considerarse ?no sedentario?, como es el caso de las auxiliares de aseo. El gasto energético es mayor en las personas que realizan actividades que implican ejercicio de baja intensidad, lo que podría ayudar a reducir los niveles de adiposidad y mantener la masa muscular de las persona

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors:A Systematic Review

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life

Reliable and secure body fall detection algorithm in a wireless mesh network

Falls in elderly is one of the most serious causes of severe injury. Lack in immediate medical help makes these injuries life threatening. An automatic fall detection system, presented in this research, would help reduce the arrival time of medical attention, reduce mortality rate and promote independent living. Therefore, the algorithm finds its application in the medical field, specifically in nursing homes. The system designed and presented in this research is not only capable of detecting human falls but also distinguishing them from routine fall-like activities. Falls are detected with the help of a small wearable embedded device, i.e. Texas Instruments\u27 eZ430 Chronos watch which is wireless development kit. The watch operates at an RF frequency of 915MHz to communicate with each other in a wireless network. The wearable wrist watch is programmable and has an in-built accelerometer sensor and microcontroller circuitry. The accelerometer sensor is motion sensitive and measures the acceleration due to gravity. Whenever a fall is detected the watch sends a signal to the neighboring watch, which is always in the monitoring mode. Signal transmission and reception between these devices is via wireless communication, where every node is a sensor forwarding the signal to the next node. A wireless mesh network helps in quick transmission of signals thereby alerting the authorities. In order to differentiate between body fall and Activities of Daily Life, various body motions and gestures have been studied and presented. The features of a real fall and that of normal human motions are extracted and analyzed from the data obtained by volunteers who participated in the research. Evaluation of results led to setting forth threshold values for parameters like acceleration, change in co-ordinate axes and angle of orientation. Over-passing the threshold raises a fall alarm to bring to the attention of the hospital authority

The Emerging Wearable Solutions in mHealth

The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed