Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements

2016 ◽  
Vol 97 (2) ◽  
pp. 224-231 ◽  
Author(s):  
Adam McLeod ◽  
Elaine M. Bochniewicz ◽  
Peter S. Lum ◽  
Rahsaan J. Holley ◽  
Geoff Emmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Wearable Sensors ◽  
Arm Movements ◽  
Learning Models ◽  
Machine Learning Models

Wearable Technology as a Source of Data-Generation Tool for Artificial Intelligence

2021 ◽  
pp. 17-34
Author(s):  
Gyasi Emmanuel Kwabena ◽  
Mageshbabu Ramamurthy ◽  
Akila Wijethunga ◽  
Purushotham Swarnalatha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Wearable Sensors ◽  
Wearable Technology ◽  
Data Generation ◽  
Learning Models ◽  
The World ◽  
Smart Wearable ◽  
The Internet Of Things ◽  
Machine Learning Models

The world is fascinated to see how technology evolves each passing day. All too soon, there's an emerging technology that is trending around us, and it is no other technology than smart wearable technology. Less attention is paid to the data that our bodies are radiating and communicating to us, but with the timely arrival of wearable sensors, we now have numerous devices that can be tracking and collecting the data that our bodies are radiating. Apart from numerous benefits that we derive from the functions provided by wearable technology such as monitoring of our fitness levels, etc., one other critical importance of wearable technology is helping the advancement of artificial intelligence (AI) and machine learning (ML). Machine learning thrives on the availability of massive data and wearable technology which forms part of the internet of things (IoT) generates megabytes of data every single day. The data generated by these wearable devices are used as a dataset for the training and learning of machine learning models. Through the analysis of the outcome of these machine learning models, scientific conclusions are made.

scholarly journals Wearable Sensors for Identifying Activity Signatures in Human-Robot Collaborative Agricultural Environments

2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Aristotelis C. Tagarakis ◽  
Lefteris Benos ◽  
Eirini Aivazidou ◽  
Athanasios Anagnostis ◽  
Dimitrios Kateris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Field Experiment ◽  
Activity Recognition ◽  
Human Activity ◽  
Wearable Sensors ◽  
Human Robot Interaction ◽  
Learning Models ◽  
Robot Interaction ◽  
Machine Learning Models

To establish a safe human–robot interaction in collaborative agricultural environments, a field experiment was performed, acquiring data from wearable sensors placed at five different body locations on 20 participants. The human–robot collaborative task presented in this study involved six well-defined continuous sub-activities, which were executed under several variants to capture, as much as possible, the different ways in which someone can carry out certain synergistic actions in the field. The obtained dataset was made publicly accessible, thus enabling future meta-studies for machine learning models focusing on human activity recognition, and ergonomics aiming to identify the main risk factors for possible injuries.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Sign in / Sign up

Export Citation Format

Share Document