scholarly journals kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 96
Author(s):  
Paulo J. S. Ferreira ◽  
João M. P. Cardoso ◽  
João Mendes-Moreira
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Maximum Size ◽  
Search Space ◽  
Human Activity Recognition ◽  
Locality Sensitive Hashing ◽  
Computationally Efficient ◽  
Online Mode ◽  
Online Classification ◽  
Memory Constraints

The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device’s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes.

scholarly journals Chord-Length Shape Features for Human Activity Recognition

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Samy Sadek ◽  
Ayoub Al-Hamadi ◽  
Bernd Michaelis ◽  
Usama Sayed
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Action Recognition ◽  
Shape Descriptor ◽  
Human Action ◽  
Human Activity Recognition ◽  
Chord Length ◽  
Shape Features ◽  
Computationally Efficient ◽  
Fuzzy Membership Functions

Despite their high stability and compactness, chord-length shape features have received relatively little attention in the human action recognition literature. In this paper, we present a new approach for human activity recognition, based on chord-length shape features. The most interesting contribution of this paper is twofold. We first show how a compact, computationally efficient shape descriptor; the chord-length shape features are constructed using 1-D chord-length functions. Second, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. On two benchmark action datasets (KTH and WEIZMANN), the approach yields promising results that compare favorably with those previously reported in the literature, while maintaining real-time performance.

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 A deep neural network model for multi-view human activity recognition

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262181
Author(s):  
Prasetia Utama Putra ◽  
Keisuke Shima ◽  
Koji Shimatani
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Recognition Rate ◽  
Human Activity Recognition ◽  
Multiple Cameras ◽  
Single View ◽  
Online Classification ◽  
Proposed Model

Multiple cameras are used to resolve occlusion problem that often occur in single-view human activity recognition. Based on the success of learning representation with deep neural networks (DNNs), recent works have proposed DNNs models to estimate human activity from multi-view inputs. However, currently available datasets are inadequate in training DNNs model to obtain high accuracy rate. Against such an issue, this study presents a DNNs model, trained by employing transfer learning and shared-weight techniques, to classify human activity from multiple cameras. The model comprised pre-trained convolutional neural networks (CNNs), attention layers, long short-term memory networks with residual learning (LSTMRes), and Softmax layers. The experimental results suggested that the proposed model could achieve a promising performance on challenging MVHAR datasets: IXMAS (97.27%) and i3DPost (96.87%). A competitive recognition rate was also observed in online classification.

scholarly journals Tracking a Subset of Skeleton Joints: An Effective Approach towards Complex Human Activity Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Latif Anjum ◽  
Stefano Rosa ◽  
Basilio Bona
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Computationally Efficient ◽  
Feature Vectors ◽  
Kinect Camera ◽  
Multiclass Classifier ◽  
Selection Of

We present a robust algorithm for complex human activity recognition for natural human-robot interaction. The algorithm is based on tracking the position of selected joints in human skeleton. For any given activity, only a few skeleton joints are involved in performing the activity, so a subset of joints contributing the most towards the activity is selected. Our approach of tracking a subset of skeleton joints (instead of tracking the whole skeleton) is computationally efficient and provides better recognition accuracy. We have developed both manual and automatic approaches for the selection of these joints. The position of the selected joints is tracked for the duration of the activity and is used to construct feature vectors for each activity. Once the feature vectors have been constructed, we use a Support Vector Machines (SVM) multiclass classifier for training and testing the algorithm. The algorithm has been tested on a purposely built dataset of depth videos recorded using Kinect camera. The dataset consists of 250 videos of 10 different activities being performed by different users. Experimental results show classification accuracy of 83% when tracking all skeleton joints, 95% when using manual selection of subset joints, and 89% when using automatic selection of subset joints.

Depth-based Human Activity Recognition: vINCI Case Study

2020 ◽  
Author(s):  
Lidia Bajenaru ◽  
Ciprian Dobre ◽  
Radu-Ioan Ciobanu ◽  
Georgiana Dedu ◽  
Silviu-George Pantelimon ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition
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