scholarly journals A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

2021 ◽  
Vol 10 (6) ◽  
pp. 3191-3201
Author(s):  
Vijaya Kumar Kambala ◽  
Harikiran Jonnadula
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Privacy Preserving ◽  
Human Activity Recognition ◽  
Human Motion ◽  
Detection Methods ◽  
Prediction Algorithm ◽  
Hybrid Prediction ◽  
Task Learning

There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.

scholarly journals Non-Linear Chaotic Features-Based Human Activity Recognition

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 111
Author(s):  
Pengjia Tu ◽  
Junhuai Li ◽  
Huaijun Wang ◽  
Ting Cao ◽  
Kan Wang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Human Motion ◽  
Sensor Data ◽  
Largest Lyapunov Exponent ◽  
Optimal Delay ◽  
Motion Time ◽  
Non Linear ◽  
Optimal Delay Time

Human activity recognition (HAR) has vital applications in human–computer interaction, somatosensory games, and motion monitoring, etc. On the basis of the human motion accelerate sensor data, through a nonlinear analysis of the human motion time series, a novel method for HAR that is based on non-linear chaotic features is proposed in this paper. First, the C-C method and G-P algorithm are used to, respectively, compute the optimal delay time and embedding dimension. Additionally, a Reconstructed Phase Space (RPS) is formed while using time-delay embedding for the human accelerometer motion sensor data. Subsequently, a two-dimensional chaotic feature matrix is constructed, where the chaotic feature is composed of the correlation dimension and largest Lyapunov exponent (LLE) of attractor trajectory in the RPS. Next, the classification algorithms are used in order to classify and recognize the two different activity classes, i.e., basic and transitional activities. The experimental results show that the chaotic feature has a higher accuracy than traditional time and frequency domain features.

Accurate human activity recognition with multi-task learning

2020 ◽  
Vol 2 (4) ◽  
pp. 288-298
Author(s):  
Yinggang Li ◽  
Shigeng Zhang ◽  
Bing Zhu ◽  
Weiping Wang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Task Learning

scholarly journals Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

2019 ◽  
Vol 5 (1) ◽  
pp. 1-9
Author(s):  
Mohammad Iqbal ◽  
Chandrawati Putri Wulandari ◽  
Wawan Yunanto ◽  
Ghaluh Indah Permata Sari
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Activity Patterns ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Sequential Patterns ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Human Activity Patterns

Discovering rare human activity patterns—from triggered motion sensors deliver peculiar information to notify people about hazard situations. This study aims to recognize rare human activities using mining non-zero-rare sequential patterns technique. In particular, this study mines the triggered motion sensor sequences to obtain non-zero-rare human activity patterns—the patterns which most occur in the motion sensor sequences and the occurrence numbers are less than the pre-defined occurrence threshold. This study proposes an algorithm to mine non-zero-rare pattern on human activity recognition called Mining Multi-class Non-Zero-Rare Sequential Patterns (MMRSP).  The experimental result showed that non-zero-rare human activity patterns succeed to capture the unusual activity. Furthermore, the MMRSP performed well according to the precision value of rare activities.

scholarly journals Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6316
Author(s):  
Dinis Moreira ◽  
Marília Barandas ◽  
Tiago Rocha ◽  
Pedro Alves ◽  
Ricardo Santos ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Short Term Memory ◽  
Indoor Localization ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Location Based Services ◽  
Sensor Data ◽  
Indoor Environments

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users’ performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users’ current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.

scholarly journals Margin-Based Deep Learning Networks for Human Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1871 ◽  
Author(s):  
Tianqi Lv ◽  
Xiaojuan Wang ◽  
Lei Jin ◽  
Yabo Xiao ◽  
Mei Song
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Human Activity Recognition ◽  
Learning Networks ◽  
Class Separation ◽  
Open Set ◽  
Challenging Research ◽  
Classification Tasks

Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have made use of such models to recognise human activities in a sensor-based manner, which have achieved good performance. However, sensor-based HAR still faces challenges; in particular, recognising similar activities that only have a different sequentiality and similarly classifying activities with large inter-personal variability. This means that some human activities have large intra-class scatter and small inter-class separation. To deal with this problem, we introduce a margin mechanism to enhance the discriminative power of deep learning networks. We modified four kinds of common neural networks with our margin mechanism to test the effectiveness of our proposed method. The experimental results demonstrate that the margin-based models outperform the unmodified models on the OPPORTUNITY, UniMiB-SHAR, and PAMAP2 datasets. We also extend our research to the problem of open-set human activity recognition and evaluate the proposed method’s performance in recognising new human activities.

scholarly journals A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2368
Author(s):  
Fatima Amjad ◽  
Muhammad Hassan Khan ◽  
Muhammad Adeel Nisar ◽  
Muhammad Shahid Farid ◽  
Marcin Grzegorzek
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Body ◽  
Human Activities ◽  
Wearable Sensors ◽  
Human Activity Recognition ◽  
Motion Sensors ◽  
Activity Data ◽  
Sensory Data ◽  
Atomic Actions

Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

scholarly journals Study on Temperature (τ) Variation for SimCLR based Activity Recognition

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Inertial Sensors ◽  
Healthcare Setting ◽  
Human Activity Recognition ◽  
Stream Data ◽  
Loss Mechanism ◽  
Overall Performance ◽  
Physiological Sensors

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. Unsupervised contrastive learning has been excellent, while the contrastive loss mechanism is less studied. In this paper, we provide a temperature (τ) variance study affecting the loss of SimCLR model and ultimately full HAR evaluation results. We focus on understanding the implications of unsupervised contrastive loss in context of HAR data. In this work, also regulation of the temperature(τ) coefficient is incorporated for improving the HAR feature qualities and overall performance for downstream tasks in healthcare setting. Performance boost of 1.3% is observed in experimentation.

scholarly journals Study on Temperature Variance for SimCLR based Activity Recognition

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Inertial Sensors ◽  
Visual Representations ◽  
Human Activity Recognition ◽  
Stream Data ◽  
Temperature Variance ◽  
Learning Technique ◽  
Physiological Sensors

Human Activity Recognition (HAR) is a process to automatically detect human activities based on stream data generated from various sensors, including inertial sensors, physiological sensors, location sensors, cameras, time, and many others. In this paper, we propose a robust SimCLR model for human activity recognition with a temperature variance study. In this work, SimCLR, a contrasting learning technique is optimized via regulating the temperature for visual representations, is incorporated for improving the HAR performance in healthcare.

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