93 research outputs found
Switchable capacitor
A micro electromechanical switchable capacitor is disclosed, comprising a substrate, a bottom elecrode, a dielaectric layer deposited on at least part of sai bottum electrode, a conductive floating electrode deposited on at least part of said dielectric layer, an armature positioned proximate to the floating electrode and a first actiuation area in order to stabilize the down state position of the armature. The device may futhermore comprise a second actuation area. The present invention provides shunt switches and series switches with actuation in zones attached to the floating electrode area of with relay actuation
Eat-Radar: Continuous Fine-Grained Eating Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network
Unhealthy dietary habits are considered as the primary cause of multiple
chronic diseases such as obesity and diabetes. The automatic food intake
monitoring system has the potential to improve the quality of life (QoF) of
people with dietary related diseases through dietary assessment. In this work,
we propose a novel contact-less radar-based food intake monitoring approach.
Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is
employed to recognize fine-grained eating and drinking gestures. The
fine-grained eating/drinking gesture contains a series of movement from raising
the hand to the mouth until putting away the hand from the mouth. A 3D temporal
convolutional network (3D-TCN) is developed to detect and segment eating and
drinking gestures in meal sessions by processing the Range-Doppler Cube (RD
Cube). Unlike previous radar-based research, this work collects data in
continuous meal sessions. We create a public dataset that contains 48 meal
sessions (3121 eating gestures and 608 drinking gestures) from 48 participants
with a total duration of 783 minutes. Four eating styles (fork & knife,
chopsticks, spoon, hand) are included in this dataset. To validate the
performance of the proposed approach, 8-fold cross validation method is
applied. Experimental results show that our proposed 3D-TCN outperforms the
model that combines a convolutional neural network and a long-short-term-memory
network (CNN-LSTM), and also the CNN-Bidirectional LSTM model (CNN-BiLSTM) in
eating and drinking gesture detection. The 3D-TCN model achieves a segmental
F1-score of 0.887 and 0.844 for eating and drinking gestures, respectively. The
results of the proposed approach indicate the feasibility of using radar for
fine-grained eating and drinking gesture detection and segmentation in meal
sessions
Eating Speed Measurement Using Wrist-Worn IMU Sensors in Free-Living Environments
Eating speed is an important indicator that has been widely scrutinized in
nutritional studies. The relationship between eating speed and several
intake-related problems such as obesity, diabetes, and oral health has received
increased attention from researchers. However, existing studies mainly use
self-reported questionnaires to obtain participants' eating speed, where they
choose options from slow, medium, and fast. Such a non-quantitative method is
highly subjective and coarse in individual level. In this study, we propose a
novel approach to measure eating speed in free-living environments
automatically and objectively using wrist-worn inertial measurement unit (IMU)
sensors. Specifically, a temporal convolutional network combined with a
multi-head attention module (TCN-MHA) is developed to detect bites (including
eating and drinking gestures) from free-living IMU data. The predicted bite
sequences are then clustered to eating episodes. Eating speed is calculated by
using the time taken to finish the eating episode to divide the number of
bites. To validate the proposed approach on eating speed measurement, a 7-fold
cross validation is applied to the self-collected fine-annotated full-day-I
(FD-I) dataset, and a hold-out experiment is conducted on the full-day-II
(FD-II) dataset. The two datasets are collected from 61 participants in
free-living environments with a total duration of 513 h, which are publicly
available. Experimental results shows that the proposed approach achieves a
mean absolute percentage error (MAPE) of 0.110 and 0.146 in the FD-I and FD-II
datasets, respectively, showcasing the feasibility of automated eating speed
measurement. To the best of our knowledge, this is the first study
investigating automated eating speed measurement
Characterization and modelling of switchable stop-band filters based on RF-MEMS and complementary split ring resonators
In this work, we present the characterization and electrical modelling of a reconfigurable stop-band filter based on RF microelectromechanical systems (RF-MEMS) combined with metamaterial structures. The device consists of a coplanar waveguide (CPW) structure that combines complementary split ring resonators (CSRRs) and RF-MEMS varactor bridges operating at Q-band. A full electrical model for the description of the proposed structure is presented. The circuit model takes into account the electrical characteristics of the RF-MEMS, CSRRs and transmission line as well as the involved electromagnetic coupling and is used for accurate prediction of switchable filter response.Peer ReviewedPostprint (author’s final draft
Reconfigurable RF-MEMS Metamaterials Filters
In this work, the design procedure, modelling and implementation of reconÂŻgurable
ÂŻlters based on RF microelectromechanical systems metamaterials is presented. SpeciÂŻcally,
tunable stop-band and pass-band frequency responses are obtained by combining RF-MEMS
with metamaterials based in complementary split rings resonators. These particles, allow for
the design of negative e®ective permittivity transmission lines, providing forbidden propagation
frequency bands. Moreover, CSRRs properly combined with metal vias in transmission lines,
generate a simultaneously " < 0 and ¹ < 0 e®ective media which involves an allowed frequency
band. These two phenomenons have been used in order to implement stop-band and pass-band
ÂŻlters. Since CSRRs present a LC-tank behaviour and are electrically coupled to the host line,
the tunability is achieved by means of the RF-MEMS, which modify the electrical characteristics
of the CSRRs and the electric coupling. A full electrical model for the description of the proposed
structures is presented. The circuit model take into account the electrical characteristics of the
RF-MEMS, CSRRs and transmission lines as well as the involved electromagnetic coupling and
are used for accurate prediction of switchable ÂŻlters response.Peer ReviewedPostprint (published version
DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network
The Otago Exercise Program (OEP) represents a crucial rehabilitation
initiative tailored for older adults, aimed at enhancing balance and strength.
Despite previous efforts utilizing wearable sensors for OEP recognition,
existing studies have exhibited limitations in terms of accuracy and
robustness. This study addresses these limitations by employing a single
waist-mounted Inertial Measurement Unit (IMU) to recognize OEP exercises among
community-dwelling older adults in their daily lives. A cohort of 36 older
adults participated in laboratory settings, supplemented by an additional 7
older adults recruited for at-home assessments. The study proposes a Dual-Scale
Multi-Stage Temporal Convolutional Network (DS-MS-TCN) designed for two-level
sequence-to-sequence classification, incorporating them in one loss function.
In the first stage, the model focuses on recognizing each repetition of the
exercises (micro labels). Subsequent stages extend the recognition to encompass
the complete range of exercises (macro labels). The DS-MS-TCN model surpasses
existing state-of-the-art deep learning models, achieving f1-scores exceeding
80% and Intersection over Union (IoU) f1-scores surpassing 60% for all four
exercises evaluated. Notably, the model outperforms the prior study utilizing
the sliding window technique, eliminating the need for post-processing stages
and window size tuning. To our knowledge, we are the first to present a novel
perspective on enhancing Human Activity Recognition (HAR) systems through the
recognition of each repetition of activities
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models
Otago Exercise Program (OEP) is a rehabilitation program for older adults to
improve frailty, sarcopenia, and balance. Accurate monitoring of patient
involvement in OEP is challenging, as self-reports (diaries) are often
unreliable. With the development of wearable sensors, Human Activity
Recognition (HAR) systems using wearable sensors have revolutionized
healthcare. However, their usage for OEP still shows limited performance. The
objective of this study is to build an unobtrusive and accurate system to
monitor OEP for older adults. Data was collected from older adults wearing a
single waist-mounted Inertial Measurement Unit (IMU). Two datasets were
collected, one in a laboratory setting, and one at the homes of the patients. A
hierarchical system is proposed with two stages: 1) using a deep learning model
to recognize whether the patients are performing OEP or activities of daily
life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a
6-second sliding window to recognize the OEP sub-classes performed. The results
showed that in stage 1, OEP could be recognized with window-wise f1-scores over
0.95 and Intersection-over-Union (IoU) f1-scores over 0.85 for both datasets.
In stage 2, for the home scenario, four activities could be recognized with
f1-scores over 0.8: ankle plantarflexors, abdominal muscles, knee bends, and
sit-to-stand. The results showed the potential of monitoring the compliance of
OEP using a single IMU in daily life. Also, some OEP sub-classes are possible
to be recognized for further analysis.Comment: 10 page
Large-scale wearable data reveal digital phenotypes for daily-life stress detection
Physiological signals have shown to be reliable indicators of stress in laboratory studies, yet large-scale ambulatory validation is lacking. We present a large-scale cross-sectional study for ambulatory stress detection, consisting of 1002 subjects, containing subjects' demographics, baseline psychological information, and five consecutive days of free-living physiological and contextual measurements, collected through wearable devices and smartphones. This dataset represents a healthy population, showing associations between wearable physiological signals and self-reported daily-life stress. Using a data-driven approach, we identified digital phenotypes characterized by self-reported poor health indicators and high depression, anxiety and stress scores that are associated with blunted physiological responses to stress. These results emphasize the need for large-scale collections of multi-sensor data, to build personalized stress models for precision medicine
Dynamic Modelling of Mental Resilience in Young Adults: Protocol for a Longitudinal Observational Study (DynaM-OBS)
Background
Stress-related mental disorders are highly prevalent and pose a substantial burden on individuals and society. Improving strategies for the prevention and treatment of mental disorders requires a better understanding of their risk and resilience factors. This multicenter study aims to contribute to this endeavor by investigating psychological resilience in healthy but susceptible young adults over 9 months. Resilience is conceptualized in this study as the maintenance of mental health or quick recovery from mental health perturbations upon exposure to stressors, assessed longitudinally via frequent monitoring of stressors and mental health.
Objective
This study aims to investigate the factors predicting mental resilience and adaptive processes and mechanisms contributing to mental resilience and to provide a methodological and evidence-based framework for later intervention studies.
Methods
In a multicenter setting, across 5 research sites, a sample with a total target size of 250 young male and female adults was assessed longitudinally over 9 months. Participants were included if they reported at least 3 past stressful life events and an elevated level of (internalizing) mental health problems but were not presently affected by any mental disorder other than mild depression. At baseline, sociodemographic, psychological, neuropsychological, structural, and functional brain imaging; salivary cortisol and α-amylase levels; and cardiovascular data were acquired. In a 6-month longitudinal phase 1, stressor exposure, mental health problems, and perceived positive appraisal were monitored biweekly in a web-based environment, while ecological momentary assessments and ecological physiological assessments took place once per month for 1 week, using mobile phones and wristbands. In a subsequent 3-month longitudinal phase 2, web-based monitoring was reduced to once a month, and psychological resilience and risk factors were assessed again at the end of the 9-month period. In addition, samples for genetic, epigenetic, and microbiome analyses were collected at baseline and at months 3 and 6. As an approximation of resilience, an individual stressor reactivity score will be calculated. Using regularized regression methods, network modeling, ordinary differential equations, landmarking methods, and neural net–based methods for imputation and dimension reduction, we will identify the predictors and mechanisms of stressor reactivity and thus be able to identify resilience factors and mechanisms that facilitate adaptation to stressors.
Results
Participant inclusion began in October 2020, and data acquisition was completed in June 2022. A total of 249 participants were assessed at baseline, 209 finished longitudinal phase 1, and 153 finished longitudinal phase 2.
Conclusions
The Dynamic Modelling of Resilience–Observational Study provides a methodological framework and data set to identify predictors and mechanisms of mental resilience, which are intended to serve as an empirical foundation for future intervention studies.
International Registered Report Identifier (IRRID)
DERR1-10.2196/3981
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