Fine-Grained Activity Recognition by Aggregating Abstract Object Usage

2005 ◽  
Author(s):  
D.J. Patterson ◽  
D. Fox ◽  
H. Kautz ◽  
M. Philipose
Keyword(s):  
Activity Recognition ◽  
Abstract Object ◽  
Fine Grained ◽  
Object Usage

Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare

2015 ◽  
Vol 19 (5) ◽  
pp. 26-35 ◽  
Author(s):  
Debraj De ◽  
Pratool Bharti ◽  
Sajal K. Das ◽  
Sriram Chellappan
Keyword(s):  
Activity Recognition ◽  
Fine Grained ◽  
Wearable Sensing

Motion and Location-Based Online Human Daily Activity Recognition

2018 ◽  
pp. 277-296
Author(s):  
Chun Zhu ◽  
Weihua Sheng
Keyword(s):  
Activity Recognition ◽  
Daily Activity ◽  
Human Subject ◽  
Viterbi Algorithm ◽  
Location Information ◽  
Motion Data ◽  
Fine Grained ◽  
Step Algorithm ◽  
Two Phases ◽  
Moderate Cost

In this chapter, the authors propose an approach to indoor human daily activity recognition that combines motion data and location information. One inertial sensor is worn on the thigh of a human subject to provide motion data while a motion capture system is used to record the human location information. Such a combination has the advantage of significantly reducing the obtrusiveness to the human subject at a moderate cost of vision processing, while maintaining a high accuracy of recognition. The approach has two phases. First, a two-step algorithm is proposed to recognize the activity based on motion data only. In the coarse-grained classification, two neural networks are used to classify the basic activities. In the fine-grained classification, the sequence of activities is modeled by a Hidden Markov Model (HMM) to consider the sequential constraints. The modified short-time Viterbi algorithm is used for real-time daily activity recognition. Second, to fuse the motion data with the location information, Bayes' theorem is used to refine the activities recognized from the motion data. The authors conduct experiments in a mock apartment, and the obtained results prove the effectiveness and accuracy of the algorithms.

Motion and Location-Based Online Human Daily Activity Recognition

2013 ◽  
pp. 304-321
Author(s):  
Chun Zhu ◽  
Weihua Sheng
Keyword(s):  
Activity Recognition ◽  
Daily Activity ◽  
Human Subject ◽  
Viterbi Algorithm ◽  
Location Information ◽  
Motion Data ◽  
Fine Grained ◽  
Step Algorithm ◽  
Two Phases ◽  
Moderate Cost

In this chapter, the authors propose an approach to indoor human daily activity recognition that combines motion data and location information. One inertial sensor is worn on the thigh of a human subject to provide motion data while a motion capture system is used to record the human location information. Such a combination has the advantage of significantly reducing the obtrusiveness to the human subject at a moderate cost of vision processing, while maintaining a high accuracy of recognition. The approach has two phases. First, a two-step algorithm is proposed to recognize the activity based on motion data only. In the coarse-grained classification, two neural networks are used to classify the basic activities. In the fine-grained classification, the sequence of activities is modeled by a Hidden Markov Model (HMM) to consider the sequential constraints. The modified short-time Viterbi algorithm is used for real-time daily activity recognition. Second, to fuse the motion data with the location information, Bayes’ theorem is used to refine the activities recognized from the motion data. The authors conduct experiments in a mock apartment, and the obtained results prove the effectiveness and accuracy of the algorithms.

scholarly journals A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures

2019 ◽  
Vol 11 (21) ◽  
pp. 2584 ◽  
Author(s):  
Yuan He ◽  
Xinyu Li ◽  
Xiaojun Jing
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Vital Role ◽  
Person Identification ◽  
Attention Network ◽  
Fine Grained ◽  
Sensing Applications ◽  
Consumption Activity ◽  
Weight Learning ◽  
The Impact

Short-range radar has become one of the latest sensor technologies for the Internet of Things (IoT), and it plays an increasingly vital role in IoT applications. As the essential task for various smart-sensing applications, radar-based human activity recognition and person identification have received more attention due to radar’s robustness to the environment and low power consumption. Activity recognition and person identification are generally treated as separate problems. However, designing different networks for these two tasks brings a high computational complexity and wastes of resources to some extent. Furthermore, there are some correlations in activity recognition and person identification tasks. In this work, we propose a multiscale residual attention network (MRA-Net) for joint activity recognition and person identification with radar micro-Doppler signatures. A fine-grained loss weight learning (FLWL) mechanism is presented for elaborating a multitask loss to optimize MRA-Net. In addition, we construct a new radar micro-Doppler dataset with dual labels of activity and identity. With the proposed model trained on this dataset, we demonstrate that our method achieves the state-of-the-art performance in both radar-based activity recognition and person identification tasks. The impact of the FLWL mechanism was further investigated, and ablation studies of the efficacy of each component in MRA-Net were also conducted.

Activity Recognition Based on RFID Object Usage for Smart Mobile Devices

2011 ◽  
Vol 26 (2) ◽  
pp. 239-246 ◽  
Author(s):  
Jaeyoung Yang ◽  
Joonwhan Lee ◽  
Joongmin Choi
Keyword(s):  
Mobile Devices ◽  
Activity Recognition ◽  
Object Usage ◽  
Smart Mobile ◽  
Smart Mobile Devices

scholarly journals UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

2017 ◽  
Author(s):  
Daniela Micucci ◽  
Marco Mobilio ◽  
Paolo Napoletano
Keyword(s):  
Activity Recognition ◽  
Human Activities ◽  
Daily Living ◽  
Ad Hoc ◽  
Coarse Grained ◽  
Specific Information ◽  
Data Sets ◽  
Fine Grained ◽  
Acceleration Data ◽  
Fitness Trackers

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with two different classifiers and with different configurations. The best results are achieved with k-NN classifying ADLs only, considering personalization, and with both windows of 51 and 151 samples.

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