Detecting User’s Behavior Shift with Sensorized Shoes and Stigmergic Perceptrons

As populations become increasingly aged, health monitoring has gained increasing importance. Recent advances in engineering of sensing, processing and artificial learning, make the development of non-invasive systems able to observe changes over time possible. In this context, the Ki-Foot project aims at developing a sensorized shoe and a machine learning architecture based on computational stigmergy to detect small variations in subjects gait and to learn and detect users behaviour shift. This paper outlines the challenges in the field and summarizes the proposed approach. The machine learning architecture has been developed and publicly released after early experimentation, in order to foster its application on real environments

The evaluation of depth image features for awakening event detection

Falls among bedridden would increase in number if they are left unsupervised by the caregivers. The aim of this study is to evaluate the features from the Kinect-like depth image representing the bedridden in detecting the awakening event as the event that falls might occur. The images from 20 subjects performing six sleeping activities including the awakening events were obtained before image segmentation based on horizontal line profile was computed to these images in localizing the bedridden as region of interest. After that, the biggest blob selection was executed in selecting the biggest blob (blob of bedridden person body). Finally, blob analysis was formulated to the resultant image before boxplot and machine learning approach called decision tree were used to analyze the output features of blob analysis. Based on the results from the boxplot analysis, it seems that centroid-x is the most dominant feature to recognize awakening event successfully as the boxplot represent the centroid-x of awakening event were not overlap with other sleeping activities. The result from machine learning approach is also seem in good agreement with boxplot analysis whereby the modelled decision tree with solely using centroid-x achieve the accuracy of 100%. The second largest accuracy is the perimeter followed by major axis length and area

Synchronous wearable wireless body sensor network composed of autonomous textile nodes

A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system

Proposta de Solução para a Mensuração de Peso por Superfície de Contato com Objetivo de Prevenir Lesões por Pressão em Pacientes Acamados

Pressure injuries (LPP) are one of the biggest adverse events foundin health services and consist of damage to the body tissues ofbedridden patients, resulting from prolonged pressure on the skin.This situation impacts on the quality of life of people who developthe condition, causing physical and emotional damage to the bedridden,in addition to increasing the time and costs of hospitalization.Based on this problem, software was developed that shows thepoints of greatest pressure between the body of a bedridden patientand the bed in which he is. This software receives information fromhardware, under development, built specifically for this project.The points of greatest pressure are made available on the screenof a monitoring application, in an organized and intuitive manner.For each person, a pressure map image is generated with the valuesread and decubitus change times are suggested through alarms. Inaddition, this image can be analyzed by a health professional whocan take steps to relieve pressure points and prevent the appearanceof LPP. As a result, in tests carried out during the research, the systembuilt showed the information successfully and the objectiveswere achieved

Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems

Real-time user localization in indoor environments is an important issue in ambient assisted living (AAL). In this context, localization based on received signal strength (RSS) has received considerable interest in the recent literature, due to its low cost and energy consumption and to its availability on all wireless communication hardware. On the other hand, the RSS-based localization is characterized by a greater error with respect to other technologies. Restricting the problem to localization of AAL users in indoor environments, we demonstrate that forecasting with a little user movement advance (for example, when the user is about to leave a room) provides significant benefits to the accuracy of RSS-based localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS measurements and trained to recognize patterns of user’s movements to feed back to the RSS-based localization syste

Posture Recognition to Prevent Bedsores for Multiple Patients Using Leaking Coaxial Cable

Leaky coaxial cable (LCX) has long been used to cover blind and semi-blind zones in wireless communication. In this paper, we propose a novel system using a LCX. The key idea is to deploy LCX and use wireless information obtained through PHY layer wireless channel state information (CSI).The core application involves identifying multiple patients’ postures in bed in order to reduce the formation of pressure ulcers or bedsores on the skin. The indoor installation and periodic recording of postures help monitor and prevent bedsores. The CSI registrations are collected using 802.11n Intel WLAN NICs. These CSI registration signatures are unique for particular posture. The amplitude variation is used for differentiating and classifying the postures for inference

A deep learning approach for pressure ulcer prevention using wearable computing

Abstract In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches

Wearable accelerometer based extended sleep position recognition

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkSleep positions have an impact on sleep quality and therefore need to be further analyzed. Current research on position tracking includes only the four basic positions. In the context of wearable devices, energy efficiency is still an open issue. This research presents a way to detect eight positions with higher granularity under energy efficient constraints. Generalized Matrix Learning Vector Quantization is used, as it is a fast and appropriate method for environments with limited computation resources, and has not been seen for this kind of application before. The overall model trained on individuals performs with an averaged accuracy of 99.8%, in contrast to an averaged accuracy of 83.62% for grouped datasets. Real world application gives an accuracy of around 98%. The results show that energy efficiency will be feasible, as performance stays similar for lower sampling rate. This is a step towards a mobile solution which gives more insight in person's sleep behaviour

Machine Learning Methods with Decision Forests for Parkinson's Detection

Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson's Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson's detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson's Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson's and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson's disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%