Natalia Valech

Bio: Natalia Valech is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 18 citations.

Preliminary results for measurement and classification of overnight wandering by dementia patient using multi-sensors

Abstract: The measurement and detection of overnight wandering is a significant issue for dementia patients and their caregivers such as a spouse The wandering places the patient at risk of injury or even death if they fall or leave their residence without being detected While it also causes stress and reduced sleep for the caregiver as they try to remain alert to the actions of their partner This paper presents initial data for the first participant from an ongoing study of dementia patients where a wander detection and diversion system based on low-cost commercial sensors has been deployed into the residence The paper shows that over a 3-week period, the analysis and classification of the sensor data is able to measure the behavior of the patient In this period, the patient only used the washroom overnight and did not wander into other parts of the residence These early results show that an off the shelf system targeted for residential security and home automation applications based on low-cost sensors supported with automated analysis and classification has the potential to be used to assist caregivers and dementia patients

Smart home technology solution for night-time wandering in persons with dementia:

Abstract: IntroductionMore than half of persons with dementia will experience night-time wandering, increasing their risk of falls and unattended home exits. This is a major predictor of caregiver burnout an...

Assistive sensor-based technology driven self-management for building resilience among people with early stage cognitive impairment

Abstract: This paper reports the technologies and workplan of the AAL RESILIEN-T project. Focused on assistive technologies, RESILIEN-T aims to improve, through self-management, the autonomy, participation in social life, and skills, of older Persons with Cognitive Impairment (PwCI) who are too often considered as “objects” of research, rather than “partners”. The study investigates existing ICT solutions to improve the self-management ability of PwCl at different stages of cognitive impairment. Sensors, devices and apps to reduce the progression of the disease are analyzed. To increase sensor capability, innovative data management, i.e. Artificial Intelligence and Machine Learning algorithms, are considered to extract significant information from the data and optimize the sensor network. Moreover, approaches to involve end-users in the development are also investigated to enhance the final outputs. The study proposes a modular and integrated platform for PwCI to self-manage various activities including nutrition, physical activities, social life, cognitive training. The choice of offering an open API to integrate wearable devices and lifestyle monitoring systems from different suppliers makes available a customable and modular product. Considering that functional decline is part of the normal aging process, it might be challenging to individuate three levels of modular architecture to increase the accuracy of the monitoring with the decline of the cognitive capabilities.

Monitoring Behaviors of Patients With Late-Stage Dementia Using Passive Environmental Sensing Approaches: A Case Series.

Abstract: Objective To show the feasibility of using different unobtrusive activity-sensing technologies to provide objective behavioral markers of persons with dementia (PwD). Design Monitored the behaviors of two PwD living in memory care unit using the Oregon Center for Aging & Technology (ORCATECH) platform, and the behaviors of two PwD living in assisted living facility using the Emerald device. Setting A memory care unit in Portland, Oregon and an assisted living facility in Framingham, Massachusetts. Participants A 63-year-old male with Alzheimer's disease (AD), and an 80-year-old female with frontotemporal dementia, both lived in a memory care unit in Portland, Oregon. An 89-year-old woman with a diagnosis of AD, and an 85-year-old woman with a diagnosis of major neurocognitive disorder, Alzheimer's type with behavioral symptoms, both resided at an assisted living facility in Framingham, Massachusetts. Measurements These include: sleep quality measured by the bed pressure mat; number of transitions between spaces and dwell times in different spaces measured by the motion sensors; activity levels measured by the wearable actigraphy device; and couch usage and limb movements measured by the Emerald device. Results Number of transitions between spaces can identify the patient's episodes of agitation; activity levels correlate well with the patient's excessive level of agitation and lack of movement when the patient received potentially inappropriate medication and neared the end of life; couch usage can detect the patient's increased level of apathy; and periodic limb movements can help detect risperidone-induced side effects. This is the first demonstration that the ORCATECH platform and the Emerald device can measure such activities. Conclusion The use of technologies for monitoring behaviors of PwD can provide more objective and intensive measurements of PwD behaviors.

WiFi Channel State Information-Based Recognition of Sitting-Down and Standing-Up Activities

Abstract: Real-time recognition of human activities is an important functionality of smart spaces. It allows a wide range of security and healthcare applications. In this work, we use the Channel State Information (CSI) of WiFi signals to assess the patterns associated with dynamic human activities, including sitting-down and standing-up actions. We preprocess raw signals with both a Hampel filter and low-pass filter. The signals are then segmented into 20-packet labelled sequences. Features including kurtosis, maximum, mean, minimum, maximum peak, skew, standard deviation, and variance are extracted for each sequence, providing feature vectors of 168 variables to enable activity recognition. Features are normalized and a series of classifiers were trained and compared to predict three activity classes: stationary (seated or standing still), sitting-down, and standing-up. Preliminary results on data collected for a single subject achieve a classification accuracy of 98.4% with a medium Gaussian Support Vector Machine (SVM) to distinguish between these three classes.

The Challenges of Connecting Smart Home Health Sensors to Cloud Analytics

Abstract: There is great potential to assess the well-being of older adults in their homes, using sensors. The data derived from these sensors can be used to create solutions that can improve the lives of the users and their family by providing knowledge of health and enable independence. The system architecture commonly proposed is based on sensors deployed in the residence to collect ambient information or information through interactive use. These sensors are then connected through the Internet to cloud services for archiving and processing that is typically based on data analytics and artificial intelligence. This paper specifically focuses on modeling these flows and presents the challenges associated with the demands they place on Internet connectivity services. The paper presents an architecture for the resulting implementation models required for these solutions to be able to be scaled in communities.