A novel monitoring system for fall detection in older people

Indexación: Scopus.This work was supported in part by CORFO - CENS 16CTTS-66390 through the National Center on Health Information Systems, in part by the National Commission for Scientific and Technological Research (CONICYT) through the Program STIC-AMSUD 17STIC-03: ‘‘MONITORing for ehealth," FONDEF ID16I10449 ‘‘Sistema inteligente para la gestión y análisis de la dotación de camas en la red asistencial del sector público’’, and in part by MEC80170097 ‘‘Red de colaboración científica entre universidades nacionales e internacionales para la estructuración del doctorado y magister en informática médica en la Universidad de Valparaíso’’. The work of V. H. C. De Albuquerque was supported by the Brazilian National Council for Research and Development (CNPq), under Grant 304315/2017-6.Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: Long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user's privacy, which constitutes an additional advantage of this alternative. © 2013 IEEE.https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=842305

Intelligent wristbands for the automatic detection of emotional states for the elderly

Over the last few years, research on computational intelligence is being conducted to detect emotional states of people. This paper proposes the use of intelligent wristbands for the automatic detection of emotional states to develop an application which allows to monitor older people in order to improve their quality of life. The paper describes the hardware design and the cognitive module that allows the recognition of the emotional states. The proposed wristband also integrates a camera that improves the emotion detection.- Programa Operacional Temático Factores de Competitividade(POCI-01-0145-). MINECO/FEDER TIN2015-65515-C4- 1-R and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon. This work is supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the projects UID/CEC/00319/2013 and Post-Doc scholarship SFRH/BPD/102696/201

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Unsupervised monitoring of an elderly person\u27s activities of daily living using Kinect sensors and a power meter

The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored. Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly. To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel. A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value. Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns. As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours. The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs