Combining Public Machine Learning Models by Using Word Embedding for Human Activity Recognition

2021 ◽  
Author(s):  
Koichi Shimoda ◽  
Akihito Taya ◽  
Yoshito Tobe
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Word Embedding ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals An Efficient Data Imputation Technique for Human Activity Recognition

2020 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Faisal Hussain ◽  
Nuno M. Garcia ◽  
Eftim Zdravevski
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Human Activity Recognition ◽  
Learning Models ◽  
K Nearest Neighbors ◽  
Daily Living Activities ◽  
Daily Life Activities ◽  
Machine Learning Models

Abstract The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.

scholarly journals Federated personalized random forest for human activity recognition

2021 ◽  
Vol 19 (1) ◽  
pp. 953-971
Author(s):  
Songfeng Liu ◽  
◽  
Jinyan Wang ◽  
Wenliang Zhang ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Activity Recognition ◽  
Human Activity ◽  
Differential Privacy ◽  
Recognition Task ◽  
Human Activity Recognition ◽  
Locality Sensitive Hashing ◽  
Learning Models ◽  
Machine Learning Models

<abstract><p>User data usually exists in the organization or own local equipment in the form of data island. It is difficult to collect these data to train better machine learning models because of the General Data Protection Regulation (GDPR) and other laws. The emergence of federated learning enables users to jointly train machine learning models without exposing the original data. Due to the fast training speed and high accuracy of random forest, it has been applied to federated learning among several data institutions. However, for human activity recognition task scenarios, the unified model cannot provide users with personalized services. In this paper, we propose a privacy-protected federated personalized random forest framework, which considers to solve the personalized application of federated random forest in the activity recognition task. According to the characteristics of the activity recognition data, the locality sensitive hashing is used to calculate the similarity of users. Users only train with similar users instead of all users and the model is incrementally selected using the characteristics of ensemble learning, so as to train the model in a personalized way. At the same time, user privacy is protected through differential privacy during the training stage. We conduct experiments on commonly used human activity recognition datasets to analyze the effectiveness of our model.</p></abstract>

scholarly journals AI Assisted Human Activity Recognition (HAR)

2019 ◽  
Vol 8 (6) ◽  
pp. 2143-2148
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Evaluation Metric ◽  
Gyroscope Sensor ◽  
The Impact ◽  
Machine Learning Models

Human Activity Recognition and assisting user on the basis of his context is attracting researchers since decade Researchers are working in the area to increase the accuracy of detection by various means. The challenging issue is to determine the correct supervised classifier for the detection purpose. This paper intent to examine the methodology used to recognize HAR and the impact of classifiers practiced in training and Testing. We have also tried to identify the suitable supervised machine learning model for HAR. Data of 30 Users with 561 features belonging to accelerometer and gyroscope sensor of smartphone from UCI repository is used for evaluation purpose. Nine different supervised machine learning Models are trained and tested on the dataset. The result concludes that HAR is a process which depends upon the classifiers used. It also conclude that out of 9 different Machine learning models ANN performs well and after that SVM, kNN, Random Forest and Extra Tree are equally good models for the purpose of HAR with Accuracy and execution time as the performance evaluation metric.

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.

Machine Learning approach applied to Human Activity Recognition – An application to the VanKasteren dataset

2021 ◽  
Vol 191 ◽  
pp. 367-372
Author(s):  
Ariza-Colpas Paola ◽  
Oñate-Bowen Alvaro Agustín ◽  
Suarez-Brieva Eydy del Carmen ◽  
Oviedo-Carrascal Ana ◽  
Urina Triana Miguel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2760
Author(s):  
Seungmin Oh ◽  
Akm Ashiquzzaman ◽  
Dongsu Lee ◽  
Yeonggwang Kim ◽  
Jinsul Kim
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Random Sampling ◽  
Data Gathering ◽  
Human Activity Recognition ◽  
Learning Models ◽  
Active Transfer ◽  
Existing Data

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

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