Assessment of Stability of MIMU Probes to Skin-Marker-Based Anatomical Reference Frames During Locomotion Tasks: Effect of Different Locations on the Lower Limb

Soft tissue artefacts (STAs) undermine the validity of skin-mounted approaches to measure skeletal kinematics. Magneto-inertial measurement units (MIMU) gained popularity due to their low cost and ease of use. Although the reliability of different protocols for marker-based joint kinematics estimation has been widely reported, there are still no indications on where to place MIMU to minimize STA. This study aims to find the most stable positions for MIMU placement, among four positions on the thigh, four on the shank, and three on the foot. Stability was investigated by measuring MIMU movements against an anatomical reference frame, defined according to a standard marker-based approach. To this aim, markers were attached both on the case of each MIMU (technical frame) and on bony landmarks (anatomical frame). For each MIMU, the nine angles between each versor of the technical frame with each versor of the corresponding anatomical frame were computed. The maximum standard deviation of these angles was assumed as the instability index of MIMU-body coupling. Six healthy subjects were asked to perform barefoot gait, step negotiation, and sit-to-stand. Results showed that (1) in the thigh, the frontal position was the most stable in all tasks, especially in gait; (2) in the shank, the proximal position is the least stable, (3) lateral or medial calcaneus and foot dorsum positions showed equivalent stability performances. Further studies should be done before generalizing these conclusions to different motor tasks and MIMU-body fixation methods. The above results are of interest for both MIMU-based gait analysis and rehabilitation approaches using wearable sensors-based biofeedback.

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An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults

Sensors ◽

10.3390/s21062050 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2050

Author(s):

Diogo Luís Marques ◽

Henrique Pereira Neiva ◽

Ivan Miguel Pires ◽

Eftim Zdravevski ◽

Martin Mihajlov ◽

...

Keyword(s):

Older Adults ◽

Low Cost ◽

Intraclass Correlation ◽

Smartphone Application ◽

Ease Of Use ◽

Systematic Bias ◽

Validity And Reliability ◽

Stand Test ◽

Sit To Stand ◽

Temporal Variables

Smartphone sensors have often been proposed as pervasive measurement systems to assess mobility in older adults due to their ease of use and low-cost. This study analyzes a smartphone-based application’s validity and reliability to quantify temporal variables during the single sit-to-stand test with institutionalized older adults. Forty older adults (20 women and 20 men; 78.9 ± 8.6 years) volunteered to participate in this study. All participants performed the single sit-to-stand test. Each sit-to-stand repetition was performed after an acoustic signal was emitted by the smartphone app. All data were acquired simultaneously with a smartphone and a digital video camera. The measured temporal variables were stand-up time and total time. The relative reliability and systematic bias inter-device were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. In contrast, absolute reliability was assessed using the standard error of measurement and coefficient of variation (CV). Inter-device concurrent validity was assessed through correlation analysis. The absolute percent error (APE) and the accuracy were also calculated. The results showed excellent reliability (ICC = 0.92–0.97; CV = 1.85–3.03) and very strong relationships inter-devices for the stand-up time (r = 0.94) and the total time (r = 0.98). The APE was lower than 6%, and the accuracy was higher than 94%. Based on our data, the findings suggest that the smartphone application is valid and reliable to collect the stand-up time and total time during the single sit-to-stand test with older adults.

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Digital Biomarker Representing Frailty Phenotypes: The Use of Machine Learning and Sensor-Based Sit-to-Stand Test

Sensors ◽

10.3390/s21093258 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3258

Author(s):

Catherine Park ◽

Ramkinker Mishra ◽

Amir Sharafkhaneh ◽

Mon S. Bryant ◽

Christina Nguyen ◽

...

Keyword(s):

Machine Learning ◽

Low Cost ◽

Wearable Sensors ◽

Area Under The Curve ◽

Frailty Model ◽

Screening Tools ◽

Physical Frailty ◽

Stand Test ◽

Sit To Stand ◽

Range Indicator

Since conventional screening tools for assessing frailty phenotypes are resource intensive and unsuitable for routine application, efforts are underway to simplify and shorten the frailty screening protocol by using sensor-based technologies. This study explores whether machine learning combined with frailty modeling could determine the least sensor-derived features required to identify physical frailty and three key frailty phenotypes (slowness, weakness, and exhaustion). Older participants (n = 102, age = 76.54 ± 7.72 years) were fitted with five wearable sensors and completed a five times sit-to-stand test. Seventeen sensor-derived features were extracted and used for optimal feature selection based on a machine learning technique combined with frailty modeling. Mean of hip angular velocity range (indicator of slowness), mean of vertical power range (indicator of weakness), and coefficient of variation of vertical power range (indicator of exhaustion) were selected as the optimal features. A frailty model with the three optimal features had an area under the curve of 85.20%, a sensitivity of 82.70%, and a specificity of 71.09%. This study suggests that the three sensor-derived features could be used as digital biomarkers of physical frailty and phenotypes of slowness, weakness, and exhaustion. Our findings could facilitate future design of low-cost sensor-based technologies for remote physical frailty assessments via telemedicine.

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Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽

10.3390/s21010194 ◽

2020 ◽

Vol 21 (1) ◽

pp. 194

Author(s):

Sarah Gonzalez ◽

Paul Stegall ◽

Harvey Edwards ◽

Leia Stirling ◽

Ho Chit Siu

Keyword(s):

Activity Recognition ◽

Principal Components ◽

Classification Accuracy ◽

Wearable Sensors ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Measurement Units ◽

The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

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Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

Scientific Data ◽

10.1038/s41597-021-00831-z ◽

2021 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Gloria Vergara-Diaz ◽

Jean-Francois Daneault ◽

Federico Parisi ◽

Chen Admati ◽

Christina Alfonso ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Symptom Severity ◽

Wearable Sensors ◽

Motor Fluctuations ◽

Sensor Data ◽

Accelerometer Data ◽

Motor Tasks ◽

Specific Symptom ◽

At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

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Imaging in Propeller Flap Surgery

Seminars in Plastic Surgery ◽

10.1055/s-0040-1715159 ◽

2020 ◽

Vol 34 (03) ◽

pp. 145-151

Author(s):

Shimpei Ono ◽

Hiroyuki Ohi ◽

Rei Ogawa

Keyword(s):

Low Cost ◽

Three Dimensional ◽

Duplex Sonography ◽

Computed Tomographic Angiography ◽

Ease Of Use ◽

Computed Tomographic ◽

Imaging Tool ◽

Preoperative Mapping ◽

Flap Design ◽

Tomographic Angiography

AbstractSince propeller flaps are elevated as island flaps and most often nourished by a single perforator nearby the defect, it is challenging to change the flap design intraoperatively when a reliable perforator cannot be found where expected to exist. Thus, accurate preoperative mapping of perforators is essential in the safe planning of propeller flaps. Various methods have been reported so far: (1) handheld acoustic Doppler sonography (ADS), (2) color duplex sonography (CDS), (3) perforator computed tomographic angiography (P-CTA), and (4) magnetic resonance angiography (MRA). To facilitate the preoperative perforator assessment, P-CTA is currently considered as the gold standard imaging tool in revealing the three-dimensional anatomical details of perforators precisely. Nevertheless, ADS remains the most widely used tool due to its low cost, faster learning, and ease of use despite an undesirable number of false-positive results. CDS can provide hemodynamic characteristics of the perforator and is a valid and safer alternative particularly in patients in whom ionizing radiation and/or contrast exposure should be limited. Although MRA is less accurate in detecting smaller perforators of caliber less than 1.0 mm and the intramuscular course of perforators at the present time, MRA is expected to improve in the future due to the recent developments in technology, making it as accurate as P-CTA. Moreover, it provides the advantage of being radiation-free with fewer contrast reactions.

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Reliability of activity monitors for physical activity assessment in patients with musculoskeletal disorders: A systematic review

Journal of Back and Musculoskeletal Rehabilitation ◽

10.3233/bmr-200348 ◽

2021 ◽

pp. 1-9

Author(s):

Francesco Negrini ◽

Alessandro de Sire ◽

Stefano Giuseppe Lazzarini ◽

Federico Pennestrì ◽

Salvatore Sorce ◽

...

Keyword(s):

Physical Activity ◽

Systematic Review ◽

Musculoskeletal Disorders ◽

Wearable Sensors ◽

Activity Monitors ◽

Activity Assessment ◽

Measurement Tools ◽

Different Types ◽

Measurement Units ◽

Full Text Screening

BACKGROUND: Activity monitors have been introduced in the last years to objectively measure physical activity to help physicians in the management of musculoskeletal patients. OBJECTIVE: This systematic review aimed at describing the assessment of physical activity by commercially available portable activity monitors in patients with musculoskeletal disorders. METHODS: PubMed, Embase, PEDro, Web of Science, Scopus and CENTRAL databases were systematically searched from inception to June 11th, 2020. We considered as eligible observational studies with: musculoskeletal patients; physical activity measured by wearable sensors based on inertial measurement units; comparisons performed with other tools; outcomes consisting of number of steps/day, activity/inactivity time, or activity counts/day. RESULTS: Out of 595 records, after removing duplicates, title/abstract and full text screening, 10 articles were included. We noticed a wide heterogeneity in the wearable devices, that resulted to be 10 different types. Patients included suffered from rheumatoid arthritis, osteoarthritis, juvenile idiopathic arthritis, polymyalgia rheumatica, and fibromyalgia. Only 3 studies compared portable activity trackers with objective measurement tools. CONCLUSIONS: Taken together, this systematic review showed that activity monitors might be considered as useful to assess physical activity in patients with musculoskeletal disorders, albeit, to date, the high device heterogeneity and the different algorithms still prevent their standardization.

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Liquid marble based digital microfluidics – fundamentals and applications

Lab on a Chip ◽

10.1039/d0lc01290d ◽

2021 ◽

Author(s):

Chin Hong Ooi ◽

Raja Vadivelu ◽

Jing Jin ◽

Sreejith Kamalalayam Rajan ◽

Pradip Singha ◽

...

Keyword(s):

Low Cost ◽

Digital Microfluidics ◽

Ease Of Use ◽

Liquid Marbles ◽

Liquid Marble

Liquid marbles are droplets with volume typically on the order of microliters coated with hydrophobic powder. The versatility, ease of use and low cost make liquid marbles an attractive platform...

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Molecularly Imprinted Polymers Combined with Electrochemical Sensors for Food Contaminants Analysis

Molecules ◽

10.3390/molecules26154607 ◽

2021 ◽

Vol 26 (15) ◽

pp. 4607

Author(s):

Dounia Elfadil ◽

Abderrahman Lamaoui ◽

Flavio Della Pelle ◽

Aziz Amine ◽

Dario Compagnone

Keyword(s):

Food Safety ◽

Molecularly Imprinted Polymers ◽

Electrochemical Sensors ◽

Low Cost ◽

Electrochemical Analysis ◽

Ease Of Use ◽

Electrochemical Sensing ◽

Food Contaminants ◽

Molecularly Imprinted ◽

Imprinted Polymers

Detection of relevant contaminants using screening approaches is a key issue to ensure food safety and respect for the regulatory limits established. Electrochemical sensors present several advantages such as rapidity; ease of use; possibility of on-site analysis and low cost. The lack of selectivity for electrochemical sensors working in complex samples as food may be overcome by coupling them with molecularly imprinted polymers (MIPs). MIPs are synthetic materials that mimic biological receptors and are produced by the polymerization of functional monomers in presence of a target analyte. This paper critically reviews and discusses the recent progress in MIP-based electrochemical sensors for food safety. A brief introduction on MIPs and electrochemical sensors is given; followed by a discussion of the recent achievements for various MIPs-based electrochemical sensors for food contaminants analysis. Both electropolymerization and chemical synthesis of MIP-based electrochemical sensing are discussed as well as the relevant applications of MIPs used in sample preparation and then coupled to electrochemical analysis. Future perspectives and challenges have been eventually given.

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Review of Electrochemical DNA Biosensors for Detecting Food Borne Pathogens

Sensors ◽

10.3390/s19224916 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4916 ◽

Cited By ~ 4

Author(s):

Qiaoyun Wu ◽

Yunzhe Zhang ◽

Qian Yang ◽

Ning Yuan ◽

Wei Zhang

Keyword(s):

Pathogen Detection ◽

Low Cost ◽

Electrochemical Techniques ◽

Ease Of Use ◽

Detection Methods ◽

Future Research ◽

Dna Biosensors ◽

Food Borne Pathogens ◽

Chip Technology ◽

Food Borne

The vital importance of rapid and accurate detection of food borne pathogens has driven the development of biosensor to prevent food borne illness outbreaks. Electrochemical DNA biosensors offer such merits as rapid response, high sensitivity, low cost, and ease of use. This review covers the following three aspects: food borne pathogens and conventional detection methods, the design and fabrication of electrochemical DNA biosensors and several techniques for improving sensitivity of biosensors. We highlight the main bioreceptors and immobilizing methods on sensing interface, electrochemical techniques, electrochemical indicators, nanotechnology, and nucleic acid-based amplification. Finally, in view of the existing shortcomings of electrochemical DNA biosensors in the field of food borne pathogen detection, we also predict and prospect future research focuses from the following five aspects: specific bioreceptors (improving specificity), nanomaterials (enhancing sensitivity), microfluidic chip technology (realizing automate operation), paper-based biosensors (reducing detection cost), and smartphones or other mobile devices (simplifying signal reading devices).

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Face recognition in an intelligent door lock with ResNet model based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189624 ◽

2021 ◽

pp. 1-11

Author(s):

Suphawimon Phawinee ◽

Jing-Fang Cai ◽

Zhe-Yu Guo ◽

Hao-Ze Zheng ◽

Guan-Chen Chen

Keyword(s):

Face Recognition ◽

Detection System ◽

Low Cost ◽

Ease Of Use ◽

Raspberry Pi ◽

Training Methods ◽

Graphics Recognition ◽

Face Images ◽

The Face ◽

Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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