Design of Semirigid Wearable Devices Based on Skin Strain Analysis

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
J. Barrios-Muriel ◽  
F. Romero Sánchez ◽  
F. J. Alonso ◽  
D. R. Salgado
Keyword(s):  
Soft Tissue ◽  
Strain Field ◽  
Strain Analysis ◽  
Wearable Devices ◽  
Wearable Device ◽  
High Interaction ◽  
Structural Parts ◽  
New Criterion ◽  
Wearable Products ◽  
Ankle Dorsiflexors

Nowadays, both usability and comfort play a key role in the development of medical and wearable products. When designing any device that is in contact with the human body, the mechanical behavior of the embraced soft tissue must be known. The unavoidable displacement of the soft tissue during motion may lead to discomfort and, thus, the removal of the wearable product. This paper presents a new methodology to design and test a wearable device based on the measurement of the dynamic skin strain field. Furthermore, from this field, the anatomical lines with minimum strain (lines of nonextension (LoNEs)) are calculated to design the structural parts of the wearable device. With this new criterion, the resulting product is not only optimized to reduce the friction in skin-device interface, but fully personalized to the patient's morphology and motion. The methodology is applied to the design of an ankle-foot wearable orthosis for subjects with ankle dorsiflexors muscles weakness due to nervous system disorders. The results confirm that the use of LoNEs may benefit the design of products with a high interaction with the skin.

Strain analysis in composite ceramics

1986 ◽  
Vol 44 ◽  
pp. 494-497
Author(s):  
W. M. Kriven
Keyword(s):  
Strain Field ◽  
Strain Analysis ◽  
Processing Temperature ◽  
Transformation Toughening ◽  
Zirconia Ceramics ◽  
Volume Increase ◽  
Analysis Technique ◽  
Fundamental Understanding ◽  
Contrast Analysis ◽  
Elastic Strain Field

Significant progress towards a fundamental understanding of transformation toughening in composite zirconia ceramics was made possible by the application of a TEM contrast analysis technique for imaging elastic strains. Spherical zirconia particles dispersed in a large-grained alumina matrix were examined by 1 MeV HVEM to simulate bulk conditions. A thermal contraction mismatch arose on cooling from the processing temperature of 1500°C to RT. Tetragonal ZrO2 contracted amisotropically with α(ct) = 16 X 10-6/°C and α(at) = 11 X 10-6/°C and faster than Al2O3 which contracted relatively isotropically at α = 8 X 10-6/°C. A volume increase of +4.9% accompanied the transformation to monoclinic symmetry at room temperature. The elastic strain field surrounding a particle before transformation was 3-dimensionally correlated with the internal crystallographic orientation of the particle and with the strain field after transformation. The aim of this paper is to theoretically and experimentally describe this technique using the ZrO2 as an example and thereby to illustrate the experimental requirements Tor such an analysis in other systems.

Strain analysis with nanometer resolution using a conventional transmission electron microsopy technique: electron diffraction contrast imaging revisited

1995 ◽  
Vol 53 ◽  
pp. 168-169
Author(s):  
Koenraad G F Janssens ◽  
Omer Van der Biest ◽  
Jan Vanhellemont ◽  
Herman E Maes ◽  
Robert Hull
Keyword(s):  
Electron Diffraction ◽  
Strain Field ◽  
Elastic Strain ◽  
Strain Analysis ◽  
Local Strain ◽  
Contrast Imaging ◽  
Strain Characterization ◽  
Physical Mechanisms ◽  
Diffraction Contrast ◽  
Transmission Electron

There is a growing need for elastic strain characterization techniques with submicrometer resolution in several engineering technologies. In advanced material science and engineering the quantitative knowledge of elastic strain, e.g. at small particles or fibers in reinforced composite materials, can lead to a better understanding of the underlying physical mechanisms and thus to an optimization of material production processes. In advanced semiconductor processing and technology, the current size of micro-electronic devices requires an increasing effort in the analysis and characterization of localized strain. More than 30 years have passed since electron diffraction contrast imaging (EDCI) was used for the first time to analyse the local strain field in and around small coherent precipitates1. In later stages the same technique was used to identify straight dislocations by simulating the EDCI contrast resulting from the strain field of a dislocation and comparing it with experimental observations. Since then the technique was developed further by a small number of researchers, most of whom programmed their own dedicated algorithms to solve the problem of EDCI image simulation for the particular problem they were studying at the time.

Comparisons of physical activity and sedentary behavior between owners and non-owners of commercial wearable devices

2021 ◽  
Vol 141 (2) ◽  
pp. 89-96
Author(s):  
Hsin-Yen Yen ◽  
Hao-Yun Huang
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Sedentary Time ◽  
Short Form ◽  
Wearable Devices ◽  
Wearable Device ◽  
World Health ◽  
World Health Organization Quality ◽  
Quality Of Life Scale

Aims: Wearable devices are a new strategy for promoting physical activity in a free-living condition that utilizes self-monitoring, self-awareness, and self-determination. The main purpose of this study was to explore health benefits of commercial wearable devices by comparing physical activity, sedentary time, sleep quality, and other health outcomes between individuals who used and those that did not use commercial wearable devices. Methods: The research design was a cross-sectional study using an Internet survey in Taiwan. Self-administered questionnaires included the International Physical Activity Questionnaire–Short Form, Pittsburgh Sleep Quality Index, Health-Promoting Lifestyle Profile, and World Health Organization Quality-of-Life Scale. Results: In total, 781 participants were recruited, including 50% who were users of wearable devices and 50% non-users in the most recent 3 months. Primary outcomes revealed that wearable device users had significantly higher self-reported walking, moderate physical activity, and total physical activity, and significantly lower sedentary time than non-users. Wearable device users had significantly better sleep quality than non-users. Conclusion: Wearable devices inspire users’ motivation, engagement, and interest in physical activity through habit formation. Wearable devices are recommended to increase physical activity and decrease sedentary behavior for promoting good health.

Validation of markerless strain-field optical tracking approach for soft tissue mechanical assessment

2021 ◽  
Vol 116 ◽  
pp. 110196
Author(s):  
Mark D. Olchanyi ◽  
Amir Sadikov ◽  
Jennifer Frattolin ◽  
Sumesh Sasidharan ◽  
M. Yousuf Salmasi ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Strain Field ◽  
Optical Tracking

Review on Reliable and Quality Wearable Healthcare Device (WHD)

2021 ◽  
Vol 10 (4) ◽  
pp. 1-25
Author(s):  
Nimi W. S. ◽  
P. Subha Hency Jose ◽  
Jegan R.
Keyword(s):  
Health Monitoring ◽  
Wearable Devices ◽  
Health Condition ◽  
Wearable Device ◽  
Smart Device ◽  
Signal Acquisition ◽  
Medical Practitioners ◽  
Quality Signal ◽  
Patient Health ◽  
Smart Wearable

This paper presents a brief review on present developments in wearable devices and their importance in healthcare networks. The state-of-the-art system architecture on wearable healthcare devices and their design techniques are reviewed and becomes an essential step towards developing a smart device for various biomedical applications which includes diseases classifications and detection, analyzing nature of the bio signals, vital parameters measurement, and e-health monitoring through noninvasive method. From the review on latest published research papers on medical wearable device and bio signal analysis, it can be concluded that it is more important and very essential to design and develop a smart wearable device in healthcare environment for quality signal acquisition and e-health monitoring which leads to effective measures of multiparameter extractions. This will help the medical practitioners to understand the nature of patient health condition easily by visualizing a quality signal by smart wearable devices.

scholarly journals Impact of a Wearable Device-Based Walking Programs in Rural Older Adults on Physical Activity and Health Outcomes: Cohort Study (Preprint)

2018 ◽  
Author(s):  
Il-Young Jang ◽  
Hae Reong Kim ◽  
Eunju Lee ◽  
Hee-Won Jung ◽  
Hyelim Park ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Health Care ◽  
Health Outcomes ◽  
Rural Areas ◽  
Wearable Devices ◽  
Community Dwelling ◽  
Wearable Device ◽  
Self Management ◽  
Community Dwelling Older Adults

BACKGROUND Community-dwelling older adults living in rural areas are in a less favorable environment for health care compared with urban older adults. We believe that intermittent coaching through wearable devices can help optimize health care for older adults in medically limited environments. OBJECTIVE We aimed to evaluate whether a wearable device and mobile-based intermittent coaching or self-management could increase physical activity and health outcomes of small groups of older adults in rural areas. METHODS To address the above evaluation goal, we carried out the “Smart Walk” program, a health care model wherein a wearable device is used to promote self-exercise particularly among community-dwelling older adults managed by a community health center. We randomly selected older adults who had enrolled in a population-based, prospective cohort study of aging, the Aging Study of Pyeongchang Rural Area. The “Smart Walk” program was a 13-month program conducted from March 2017 to March 2018 and included 6 months of coaching, 1 month of rest, and 6 months of self-management. We evaluated differences in physical activity and health outcomes according to frailty status and conducted pre- and postanalyses of the Smart Walk program. We also performed intergroup analysis according to adherence of wearable devices. RESULTS We recruited 22 participants (11 robust and 11 prefrail older adults). The two groups were similar in most of the variables, except for age, frailty index, and Short Physical Performance Battery score associated with frailty criteria. After a 6-month coaching program, the prefrail group showed significant improvement in usual gait speed (mean 0.73 [SD 0.11] vs mean 0.96 [SD 0.27], P=.02), International Physical Activity Questionnaire scores in kcal (mean 2790.36 [SD 2224.62] vs mean 7589.72 [SD 4452.52], P=.01), and European Quality of Life-5 Dimensions score (mean 0.84 [SD 0.07] vs mean 0.90 [SD 0.07], P=.02), although no significant improvement was found in the robust group. The average total step count was significantly different and was approximately four times higher in the coaching period than in the self-management period (5,584,295.83 vs 1,289,084.66, P<.001). We found that participants in the “long-self” group who used the wearable device for the longest time showed increased body weight and body mass index by mean 0.65 (SD 1.317) and mean 0.097 (SD 0.513), respectively, compared with the other groups. CONCLUSIONS Our “Smart Walk” program improved physical fitness, anthropometric measurements, and geriatric assessment categories in a small group of older adults in rural areas with limited resources for monitoring. Further validation through various rural public health centers and in a large number of rural older adults is required.

scholarly journals A new methodology to identify minimum strain anatomical lines based on 3-D digital image correlation

2017 ◽  
Vol 8 (2) ◽  
pp. 337-347 ◽  
Author(s):  
Jorge Barrios-Muriel ◽  
Francisco Javier Alonso Sánchez ◽  
David Rodríguez Salgado ◽  
Francisco Romero-Sánchez
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
Strain Field ◽  
Three Dimensional ◽  
Wearable Devices ◽  
Plane Motion ◽  
Human Motion ◽  
The Body ◽  
Image Correlation ◽  
Minimum Deformation

Abstract. Today there is continuous development of wearable devices in various fields such as sportswear, orthotics and personal gadgets, among others. The design of these devices involves the human body as a support environment. Based on this premise, the development of wearable devices requires an improved understanding of the skin strain field of the body segment during human motion. This paper presents a methodology based on a three dimensional digital image correlation (3D-DIC) system to measure the skin strain field and to estimate anatomical lines with minimum deformation as design criteria for the aforementioned wearable devices. The errors of displacement and strain measurement related to 3-D reconstruction and out-of-plane motion are investigated and the results are acceptable in the case of large deformation. This approach can be an effective tool to improve the design of wearable devices in the clinical orthopaedics and ergonomics fields, where comfort plays a key role in supporting the rehabilitation process.

scholarly journals Strain Analysis of the Nuozhadu High Rockfill Dam during Initial Impoundment

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Xiaolong Lv ◽  
Shichun Chi
Keyword(s):  
Shear Strain ◽  
Strain Field ◽  
Tensile Strain ◽  
Strain Analysis ◽  
Smoothing Method ◽  
Regulatory Agencies ◽  
Strain Concentration ◽  
Rockfill Dam ◽  
High Rockfill Dam

The safety of rockfill dams during initial impoundment has always been an issue of interest for regulatory agencies. Specifically, it is necessary to identify potential tensile strain zones and shear strain concentration zones in which cracks may form. In this paper, a meshless smoothing method is proposed to construct the strain field of a prototype dam based on monitoring displacement data. For verification, this method is applied to calculate the strain field of the Nuozhadu core wall rockfill dam. The results show that the proposed method can provide regulatory agencies with an effective tool for dam inspection during initial impoundment.

scholarly journals Dear Watch, Should I get a COVID Test? Designing deployable machine learning for wearables

2021 ◽  
Author(s):  
Anna Goldenberg ◽  
Bret Nestor ◽  
Jaryd Hunter ◽  
Raghu Kainkaryam ◽  
Erik Drysdale ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Real World ◽  
False Positive Rate ◽  
Wearable Devices ◽  
Classification Performance ◽  
Wearable Device ◽  
Screening Tools ◽  
Machine Learning Model ◽  
Positive Rate

Abstract Commercial wearable devices are surfacing as an appealing mechanism to detect COVID-19 and potentially other public health threats, due to their widespread use. To assess the validity of wearable devices as population health screening tools, it is essential to evaluate predictive methodologies based on wearable devices by mimicking their real-world deployment. Several points must be addressed to transition from statistically significant differences between infected and uninfected cohorts to COVID-19 inferences on individuals. We demonstrate the strengths and shortcomings of existing approaches on a cohort of 32,198 individuals who experience influenza like illness (ILI), 204 of which report testing positive for COVID-19. We show that, despite commonly made design mistakes resulting in overestimation of performance, when properly designed wearables can be effectively used as a part of the detection pipeline. For example, knowing the week of year, combined with naive randomised test set generation leads to substantial overestimation of COVID-19 classification performance at 0.73 AUROC. However, an average AUROC of only 0.55 +/- 0.02 would be attainable in a simulation of real-world deployment, due to the shifting prevalence of COVID-19 and non-COVID-19 ILI to trigger further testing. In this work we show how to train a machine learning model to differentiate ILI days from healthy days, followed by a survey to differentiate COVID-19 from influenza and unspecified ILI based on symptoms. In a forthcoming week, models can expect a sensitivity of 0.50 (0-0.74, 95% CI), while utilising the wearable device to reduce the burden of surveys by 35%. The corresponding false positive rate is 0.22 (0.02-0.47, 95% CI). In the future, serious consideration must be given to the design, evaluation, and reporting of wearable device interventions if they are to be relied upon as part of frequent COVID-19 or other public health threat testing infrastructures.

scholarly journals Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study

10.2196/19732 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e19732
Author(s):  
Ben Kim ◽  
Sandra M McKay ◽  
Joon Lee
Keyword(s):  
Older Adults ◽  
Heart Rate ◽  
Logistic Regression ◽  
Home Care ◽  
Total Sleep Time ◽  
Care Service ◽  
Sleep Time ◽  
Wearable Devices ◽  
Wearable Device ◽  
Deep Sleep

Background Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population. Objective The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible. Methods From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty. Results A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88). Conclusions We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services.

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