14,105 research outputs found
Deep human activity recognition using wearable sensors
This paper addresses the problem of classifying motion signals acquired via wearable sensors for the recognition of human activity. Automatic and accurate classification of motion signals is important in facilitating the development of an effective automated health monitoring system for the elderlies. Thus, we gathered hip motion signals from two different waist mounted sensors and for each individual sensor, we converted the motion signal into spectral image sequence. We use these images as inputs to independently train two Convolutional Neural Networks (CNN), one for each of the generated image sequences from the two sensors. The outputs of the trained CNNs are then fused together to predict the final class of the human activity. We evaluate the performance of the proposed method using the cross-subjects testing approach. Our method achieves recognition accuracy (F1 score) of 0.87 on a publicly available real-world human activity dataset. This performance is superior to that reported by another state-of-the-art method on the same dataset
Human activity recognition using wearable sensors: a deep learning approach
In the past decades, Human Activity Recognition (HAR) grabbed considerable research attentions from a wide range of pattern recognition and human–computer interaction researchers due to its prominent applications such as smart home health care. The wealth of information requires efficient classification and analysis methods. Deep learning represents a promising technique for large-scale data analytics. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among them, physical human activity recognition through wearable sensors provides valuable information about an individual’s degree of functional ability and lifestyle. There is abundant research that works upon real time processing and causes more power consumption of mobile devices. Mobile phones are resource-limited devices. It is a thought-provoking task to implement and evaluate different recognition systems on mobile devices.
This work proposes a Deep Belief Network (DBN) model for successful human activity recognition. Various experiments are performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN performs competitively in comparison with other algorithms and achieves satisfactory activity recognition performance. Some open problems and ideas are also presented and should be investigated as future research
Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition
Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation
CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors
In this paper, we report a hierarchical deep learning model for
classification of complex human activities using motion sensors. In contrast to
traditional Human Activity Recognition (HAR) models used for event-based
activity recognition, such as step counting, fall detection, and gesture
identification, this new deep learning model, which we refer to as CHARM
(Complex Human Activity Recognition Model), is aimed for recognition of
high-level human activities that are composed of multiple different low-level
activities in a non-deterministic sequence, such as meal preparation, house
chores, and daily routines. CHARM not only quantitatively outperforms
state-of-the-art supervised learning approaches for high-level activity
recognition in terms of average accuracy and F1 scores, but also automatically
learns to recognize low-level activities, such as manipulation gestures and
locomotion modes, without any explicit labels for such activities. This opens
new avenues for Human-Machine Interaction (HMI) modalities using wearable
sensors, where the user can choose to associate an automated task with a
high-level activity, such as controlling home automation (e.g., robotic vacuum
cleaners, lights, and thermostats) or presenting contextually relevant
information at the right time (e.g., reminders, status updates, and
weather/news reports). In addition, the ability to learn low-level user
activities when trained using only high-level activity labels may pave the way
to semi-supervised learning of HAR tasks that are inherently difficult to
label.Comment: 8 pages, 5 figure
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation
Wearable sensor-based human activity recognition (HAR) has emerged as a
principal research area and is utilized in a variety of applications. Recently,
deep learning-based methods have achieved significant improvement in the HAR
field with the development of human-computer interaction applications. However,
they are limited to operating in a local neighborhood in the process of a
standard convolution neural network, and correlations between different sensors
on body positions are ignored. In addition, they still face significant
challenging problems with performance degradation due to large gaps in the
distribution of training and test data, and behavioral differences between
subjects. In this work, we propose a novel Transformer-based Adversarial
learning framework for human activity recognition using wearable sensors via
Self-KnowledgE Distillation (TASKED), that accounts for individual sensor
orientations and spatial and temporal features. The proposed method is capable
of learning cross-domain embedding feature representations from multiple
subjects datasets using adversarial learning and the maximum mean discrepancy
(MMD) regularization to align the data distribution over multiple domains. In
the proposed method, we adopt the teacher-free self-knowledge distillation to
improve the stability of the training procedure and the performance of human
activity recognition. Experimental results show that TASKED not only
outperforms state-of-the-art methods on the four real-world public HAR datasets
(alone or combined) but also improves the subject generalization effectively.Comment: 17 pages, 5 figures, Submitted to Knowledge-Based Systems, Elsevier.
arXiv admin note: substantial text overlap with arXiv:2110.1216
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