scholarly journals Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1101 ◽  
Author(s):  
Angela Sucerquia ◽  
José López ◽  
Jesús Vargas-Bonilla
Keyword(s):  
Real Time ◽  
Real Life ◽  
Fall Detection ◽  
Triaxial Accelerometer

scholarly journals Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

2018 ◽  
Author(s):  
Angela Sucerquia ◽  
Jose David López ◽  
Francisco Vargas-Bonilla
Keyword(s):  
False Positive Rate ◽  
Sampling Rate ◽  
Elderly Person ◽  
Real Life ◽  
Medical Personnel ◽  
Fall Detection ◽  
Target Population ◽  
Elderly Persons ◽  
Triaxial Accelerometer ◽  
Classification Feature

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

scholarly journals Kalman Filter Based Elderly Fall Detection with a Triaxial Accelerometer

2017 ◽  
Author(s):  
Angela Sucerquia ◽  
Jose David Lόpez ◽  
Francisco Vargas-Bonilla
Keyword(s):  
Kalman Filter ◽  
False Positive Rate ◽  
Sampling Rate ◽  
Elderly Person ◽  
Real Life ◽  
Medical Personnel ◽  
Fall Detection ◽  
Elderly Persons ◽  
Triaxial Accelerometer ◽  
Positive Rate

The consequences of a fall on an elderly person can be diminished if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the objective population, or are not feasible to be implemented in real-life conditions. In this work we propose a Kalman-filter-based fall detection methodology that includes a periodicity detector to reduce the false positive rate. Moreover, this methodology requires a sampling rate of only 25 Hz, it does not require large computations or memory, and it is robust among devices. We tested our approach with the SisFall dataset. Then, we validated it with a new round of simulated activities with young adults and an elderly person achieving 99.4 % of accuracy. Finally, we gave the devices to three elderly persons during two days for full-day validations. They continued with their normal life and the devices behaved as expected.

scholarly journals Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer

2018 ◽  
Author(s):  
Angela Sucerquia ◽  
Jose David Lopez ◽  
Francisco Vargas-Bonilla
Keyword(s):  
False Positive Rate ◽  
Sampling Rate ◽  
Elderly Person ◽  
Real Life ◽  
Medical Personnel ◽  
Fall Detection ◽  
Target Population ◽  
Elderly Persons ◽  
Triaxial Accelerometer ◽  
Classification Feature

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals A Probabilistic Model for Real-Time Semantic Prediction of Human Motion Intentions from RGBD-Data

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4141
Author(s):  
Wouter Houtman ◽  
Gosse Bijlenga ◽  
Elena Torta ◽  
René van de Molengraft
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Probabilistic Model ◽  
Real Life ◽  
Human Motion ◽  
Model Based ◽  
Multiple Hypotheses ◽  
Navigation Algorithms ◽  
The Right ◽  
Motion Behavior

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.

Robust spatio-temporal descriptors for real-time SVM-based fall detection

2014 ◽  
Author(s):  
Imen Charfi ◽  
Johel Miteran ◽  
Julien Dubois ◽  
Barthelemy Heyrman ◽  
Mohamed Atri
Keyword(s):  
Real Time ◽  
Fall Detection ◽  
Spatio Temporal

scholarly journals Home-based Cardiac Rehabilitation - the real barriers of programs at distance

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
J Brito ◽  
I Aguiar-Ricardo ◽  
P Alves Da Silva ◽  
B Valente Da Silva ◽  
N Cunha ◽  
...  
Keyword(s):  
Exercise Training ◽  
Cardiac Rehabilitation ◽  
Real Time ◽  
Training Session ◽  
Real Life ◽  
Participation Rate ◽  
Exercise Session ◽  
Exterior Space ◽  
Supervised Exercise ◽  
Home Based

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Despite the established benefits of cardiac rehabilitation (CR), it remains significantly underutilized. Home-based CR (CR-HB) programs should offer the same core CR components as Centre-based programs (CR-CB) but several aspects need to be adapted, communication and supervision must be improved. Although CR-HB has been successfully deployed and is a valuable alternative to CR-CB, there is less structured experience with these non-uniform programs and further studies are needed to understand which patients (pts) are indicated to this type of program. Purpose To investigate pt-perceived facilitators and barriers to home-based rehabilitation exercise. Methods Prospective cohort study which included pts who were participating in a CR-CB program and accepted to participate in a CR-HB program after CR-CB closure due to COVID-19. The CR-HB consisted in a multidisciplinary digital CR program, including pt risk evaluation and regular assessment, exercise, educational and psychological sessions. The online exercise training sessions consisted of recorded videos and real time online supervised exercise training group sessions. It was recommended to do each session 3 times per week, during 60 min. A pictorial exercise training guidebook was available to all participants including instructions regarding safety, clothing and warm-up, and a detailed illustrated description of each  exercise sessions. Also, for questions or difficulties regarding the exercises, an e-mail and telephone was provided. Once a month, real time CR exercise sessions was provided with a duration of 60min. Results 116 cardiovascular disease pts (62.6 ± 8.9years, 95 males) who were attending a face-to-face CR program were included in a CR-HB program. The majority of the pts had coronary artery disease (89%) and 5% valvular disease. Regarding risk factors, obesity was the most common (75%) followed by hypertension (60%), family history (42%), dyslipidaemia (38%), diabetes (18%), and smoking (13%). Almost half (47%) of the participants did at least one online exercise training session per week: 58% did 2-3 times per week, 27% once per week and 15% more than 4 times per week. Participants who did less than one exercise session per week reported as cause: lack of motivation (38%), preference of a different mode of exercise training such as exercise in the exterior space (26%), technology barrier such as impossibility to stream online videos (11%), fear of performing exercise without supervision (4%), and limited space at home (4%). Conclusions Our study based on real-life results of a CR-HB program shows a sub-optimal rate of participation in exercise sessions due to different causes, but mainly for the lack of motivation to exercise alone or preference for walking in exterior space. The knowledge of the CR-HB program barriers will facilitate to find out strategies to increase the participation rate and to select the best candidates for this type of programs.

Radar Based Real-Time Fall Detection System with Low Power Consumption

2021 ◽  
Author(s):  
Jincheng Lu ◽  
Zixuan Ou ◽  
Ziyu Liu ◽  
Cheng Han ◽  
Wenbin Ye
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Low Power Consumption ◽  
Time Fall
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