scholarly journals Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5206
Author(s):  
Enida Cero Dinarević ◽  
Jasmina Baraković Husić ◽  
Sabina Baraković
Keyword(s):  
Energy Consumption ◽  
Data Collection ◽  
Activity Recognition ◽  
Human Activity ◽  
Target Population ◽  
Human Activity Recognition ◽  
Comprehensive Review ◽  
Health And Wellbeing ◽  
Data Segmentation ◽  
Human Motions

Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

scholarly journals On-Device Deep Learning Inference for Efficient Activity Data Collection

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3434 ◽  
Author(s):  
Nattaya Mairittha ◽  
Tittaya Mairittha ◽  
Sozo Inoue
Keyword(s):  
Deep Learning ◽  
Data Collection ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Human Activity Recognition ◽  
Classification Model ◽  
Activity Data ◽  
Ground Truth Data ◽  
Recognition Systems

Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. To enable us to perform evaluations, we conduct the experiments with two conditional methods. We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications. By evaluating with the dataset gathered, the results show our proposed method has improvements in both data quality (i.e., the performance of a classification model) and data quantity (i.e., the number of data collected) that reflect our method could improve activity data collection, which can enhance human activity recognition systems. We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish the preliminary dataset collected to the research community for activity recognition.

scholarly journals Rate-Invariant Modeling in Lie Algebra for Activity Recognition

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1888
Author(s):  
Malek Boujebli ◽  
Hassen Drira ◽  
Makram Mestiri ◽  
Imed Riadh Farah
Keyword(s):  
Computer Vision ◽  
Lie Algebra ◽  
Activity Recognition ◽  
Human Activity ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
High Complexity ◽  
Human Motions ◽  
Inter Frame ◽  
Automatic Systems

Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. In this paper, we propose to model the inter-frame rigid evolution of skeleton parts as the trajectory in the Lie group SE(3)×…×SE(3). The motion of the object is similarly modeled as an additional trajectory in the same manifold. The classification is performed based on a rate-invariant comparison of the resulting trajectories mapped to a vector space, the Lie algebra. Experimental results on three action and activity datasets show that the proposed method outperforms various state-of-the-art human activity recognition approaches.

scholarly journals Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 89
Author(s):  
Florian Grützmacher ◽  
Albert Hein ◽  
Thomas Kirste ◽  
Christian Haubelt
Keyword(s):  
Energy Consumption ◽  
Activity Recognition ◽  
Human Activity ◽  
Energy Efficient ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
System Level ◽  
Estimation Methods ◽  
Level Energy ◽  
Wearable Sensor

The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

SenseCollect

2021 ◽  
Vol 5 (3) ◽  
pp. 1-27
Author(s):  
Wenqiang Chen ◽  
Shupei Lin ◽  
Elizabeth Thompson ◽  
John Stankovic
Keyword(s):  
Data Collection ◽  
Activity Recognition ◽  
Human Activity ◽  
Predictive Models ◽  
Large Scale ◽  
Human Subjects ◽  
Control Variable ◽  
Human Activity Recognition ◽  
Complicating Factors ◽  
Student Researchers

On-body sensor-based human activity recognition (HAR) lags behind other fields because it lacks large-scale, labeled datasets; this shortfall impedes progress in developing robust and generalized predictive models. To facilitate researchers in collecting more extensive datasets quickly and efficiently we developed SenseCollect. We did a survey and interviewed student researchers in this area to identify what barriers are making it difficult to collect on-body sensor-based HAR data from human subjects. Every interviewee identified data collection as the hardest part of their research, stating it was laborious, consuming and error-prone. To improve HAR data resources we need to address that barrier, but we need a better understanding of the complicating factors to overcome it. To that end we conducted a series of control variable experiments that tested several protocols to ascertain their impact on data collection. SenseCollect studied 240+ human subjects in total and presented the findings to develop a data collection guideline. We also implemented a system to collect data, created the two largest on-body sensor-based human activity datasets, and made them publicly available.

scholarly journals Human Activity Recognition Based on Quantization on Feature’s Classification Capability

2016 ◽  
Author(s):  
Cheng Xu ◽  
Xiaotong Zhang ◽  
Jie He
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Wearable Sensors ◽  
Feature Selection Method ◽  
Physical Nature ◽  
Classification Problem ◽  
Human Activity Recognition ◽  
Data Dependencies ◽  
Human Motions ◽  
Classification Capability

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

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