scholarly journals Mechatronics Enabling Kit for 3D Printed Hand Prosthesis

2019 ◽  
Vol 1 (1) ◽  
pp. 769-778
Author(s):  
Tat Hang Wong ◽  
Davide Asnaghi ◽  
Suk Wai Winnie Leung
Keyword(s):  
Wearable Sensors ◽  
Sensor Data ◽  
Smart Devices ◽  
User Centered Design ◽  
User Intention ◽  
Engineering Research ◽  
Key Innovation ◽  
3D Printed ◽  
High Degree ◽  
Research Endeavor

AbstractNew advances in both neurosciences and computational approaches have changed the landscapes for smart devices design serving mobility-related disabilities. In this paper we present the integration of affordable robotics and wearable sensors through our mechatronic product platform, Sparthan, to enable accessibility of the technology in both the power prosthesis and neurorehabilitation space. Sparthan leverages 3rd party EMG sensors, Myo armband, to process muscles sensor data and translate user intention into hand movements. Key innovation includes the modularity, scalability and high degree of customization the solution affords to the target users. User-centered design approaches and mechatronic system design are detailed to demonstrate the versatility of integrative systems and design. What started off as an engineering research endeavor is also positioned to be deployed to deliver real-world impact, especially for prosthesis users in developing countries.

scholarly journals Employing entropy measures to identify visitors in multi-occupancy environments

2020 ◽  
Author(s):  
Aadel Howedi ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Home Environment ◽  
Daily Living ◽  
Wearable Sensors ◽  
Permutation Entropy ◽  
Care Workers ◽  
Entropy Measures ◽  
Novel Method ◽  
High Degree ◽  
Motion Detectors ◽  
To Receive

AbstractHuman activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

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

Sensors ◽  
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.

3D Printed Smart Silk Wearable Sensors

The Analyst ◽  
2021 ◽  
Author(s):  
Tianshu Chu ◽  
Huili Wang ◽  
Yumeng Qiu ◽  
Haoxi Luo ◽  
Bingfang He ◽  
...  
Keyword(s):  
Point Of Care ◽  
Wearable Sensors ◽  
Point Of Care Testing ◽  
Biochemical Sensing ◽  
3D Printed ◽  
Physiological And Biochemical

Wearable sensors play a key role in point-of-care testing (POCT) for its flexible and integration capability on sensitive physiological and biochemical sensing. Here, we present a multifunction wearable silk patch...

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

2021 ◽  
Vol 8 (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.

scholarly journals Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment

2021 ◽  
Vol 11 (13) ◽  
pp. 6197
Author(s):  
Alexandros A. Lavdas ◽  
Nikos A. Salingaros ◽  
Ann Sussman
Keyword(s):  
Visual Attention ◽  
Eye Tracking ◽  
Wearable Sensors ◽  
Computer Gaming ◽  
Geometric Patterns ◽  
Relative Design ◽  
Fixation Points ◽  
High Degree ◽  
Artificial Intelligence Application ◽  
Intelligence Application

Eye-tracking technology is a biometric tool that has found many commercial and research applications. The recent advent of affordable wearable sensors has considerably expanded the range of these possibilities to fields such as computer gaming, education, entertainment, health, neuromarketing, psychology, etc. The Visual Attention Software by 3M (3M-VAS) is an artificial intelligence application that was formulated using experimental data from eye-tracking. It can be used to predict viewer reactions to images, generating fixation point probability maps and fixation point sequence estimations, thus revealing pre-attentive processing of visual stimuli with a very high degree of accuracy. We have used 3M-VAS software in an innovative implementation to analyze images of different buildings, either in their original state or photographically manipulated, as well as various geometric patterns. The software not only reveals non-obvious fixation points, but also overall relative design coherence, a key element of Christopher Alexander’s theory of geometrical order. A more evenly distributed field of attention seen in some structures contrasts with other buildings being ignored, those showing instead unconnected points of splintered attention. Our findings are non-intuitive and surprising. We link these results to both Alexander’s theory and Neuroscience, identify potential pitfalls in the software’s use, and also suggest ways to avoid them.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020 ◽  
Vol 10 (20) ◽  
pp. 7122
Author(s):  
Ahmad Jalal ◽  
Mouazma Batool ◽  
Kibum Kim
Keyword(s):  
Physical Activity ◽  
Human Health ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Tree Classifier ◽  
Combined Features

The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.

scholarly journals Measuring social networks in primates: wearable sensors versus direct observations

2020 ◽  
Vol 476 (2236) ◽  
pp. 20190737
Author(s):  
Valeria Gelardi ◽  
Jeanne Godard ◽  
Dany Paleressompoulle ◽  
Nicolas Claidiere ◽  
Alain Barrat
Keyword(s):  
Data Collection ◽  
Wearable Sensors ◽  
A Priori ◽  
Population Level ◽  
Sensor Data ◽  
Collection Method ◽  
Data Collection Method ◽  
The Social ◽  
Different Types ◽  
Data Yield

Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioural observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Moreover, sensor data yield a reliable social network over short time scales and can be used for long-term studies, showing their important potential for detailed studies of the evolution of animal social groups.

scholarly journals Biomaterials – Novel Advances in Nasal Medical Implants, 3D Printing Applications

2021 ◽  
pp. 52-64
Author(s):  
T. M. Amulya ◽  
K. G. Siree ◽  
T. M. Pramod Kumar ◽  
M. B. Bharathi ◽  
K. Divith ◽  
...  
Keyword(s):  
3D Printing ◽  
Tissue Loss ◽  
Medical Implants ◽  
Anatomical Structures ◽  
Regulatory Aspects ◽  
3D Printed ◽  
Targeting Ability ◽  
Soft Tissue Loss ◽  
Biodegradable Biomaterials ◽  
High Degree

The scope and applications of biomaterials have spread out throughout a broad spectrum. Particularly in pharmacy, biomaterials are an attractive choice because they can be modified to decrease toxicity, increase the targeting ability among many other aspects of drug delivery. Extensive studies have led to the development of many metal-based, ceramic, biocompatible and biodegradable biomaterials for medical purposes among many others. The utilization of 3D printing in this discipline is a very novel research subject with infinite potential. Personalized and customized nasal implants are a great option to increase patient compliance and 3D printed accurate anatomical structures are rendered to be effective tools of learning. One of the disadvantages of biomaterial-based implants is the formation of a thick fibrous capsule formation around the implant, others being breakage, soft tissue loss and so on. Regulatory aspects are less explored for nasal implants. 3D printing is a unique technique that allows for a high degree of customisation in pharmacy, dentistry and in designing of medical devices. Current research in 3D printing indicates towards reproducing an organ in the form of a chip; paving the way for more studies and opportunities to perfecting the existing technique.

The Negative Effects of Using Smart Devices on the Physical and Psychological Health of Palestinian Children aged (13-16) years from their Perspective

2020 ◽  
Vol 8 (3) ◽  
pp. 01-19
Author(s):  
Nuha Iter
Keyword(s):  
Psychological Health ◽  
Smart Devices ◽  
Smart Device ◽  
Negative Effects ◽  
Physical And Psychological Health ◽  
Palestinian Children ◽  
Open Question ◽  
High Degree ◽  
The Relationship ◽  
Descriptive Method

The study aimed to explore the negative effects of using smart devices on the physical and psychological health of children aged (13-16) years from their perspective. The study was applied to a random sample of children aged (13-16), consisting of (102) male and female students. The descriptive method was used to answer the study questions, and a questionnaire was developed to collect data, which contains (3) sections, first section asked about the most used and preferred devices by children aged (13-16) years, and the number of hours the child used the smart device, the second one asked about the negative effects of using the smart devices on the physical and psychological health of children aged (13-16) years from their perspective, and the third section is an open question to know other negative effects of using the smart devices on the physical and psychological health of children aged (13-16) years. The study achieved a set of results, such as the smartphones are the most used and preferred devices by children aged (13-16) years, where (57%) of the study sample preferred to use, and there is  (86.3%) of children aged (13-16) use these devices at average from 4 up to 6 hours daily.  The responders highly agreed upon the negative effects of the use of smart devices on the physical health with average (4.2); which is a high degree, also the responders highly agreed upon the negative effects of  the use of smart devices on the physiological health with average  is  (3.73) which is also high,  added there are other effects caused by the use of smart devices for long hours on  children aged (13-16); the low rate of family discussions, and causes the low writing skills for child.   Depending on the results of the study, the researcher recommends that:  researchers should conduct a correlative study to know the relationship between the effects and the number of hours of daily use of devices; families should rationalize the use of smart devices.

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