Chronic Pain Protective Behavior Detection with Deep Learning

Wang, Chongyang; Olugbade, Temitayo; Mathur, Akhil; Williams, Amanda C de C; Lane, Nicholas D.; Bianchi-Berthouze, Nadia

doi:10.1145/3449068

Cited by 11 publications

(7 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers working on the EmoPain database [6] found that conventional and deep learning methods for analysing human movement are viable tools for detecting pain-related protective behaviours [32], [49], [50]. Wang et al [49], [50], [52] demonstrated that using LSTM-based architectures allows for activity-independent pain-related behaviour detection (PBD) with improved performance. In [49], [50], stacked LSTM and dual-stream LSTM were studied for processing of body movement data in conjunction with data augmentation and segmentation window width approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wang et al [49], [50], [52] demonstrated that using LSTM-based architectures allows for activity-independent pain-related behaviour detection (PBD) with improved performance. In [49], [50], stacked LSTM and dual-stream LSTM were studied for processing of body movement data in conjunction with data augmentation and segmentation window width approaches. Three LSTM layers and two sets of three LSTM layers (for the MoCap and sEMG streams) were used for stacked-LSTM and dual-stream LSTM, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…1) The duration of recorded activities varies per individual, as each individual adjusts their movement patterns based on their needs to minimise energy expenditure while maximising protective behaviour [11], [50], [52]. The variety in the duration of recorded activities is considerably larger than the number of images in benchmark video databases for human activity recognition (HAR) [29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

Dehshibi

Olugbade

Díaz-de-María

et al. 2023

IEEE J. Sel. Top. Signal Process.

Self Cite

View full text Add to dashboard Cite

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

Dehshibi

Olugbade

Díaz-de-María

et al. 2023

IEEE J. Sel. Top. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Rashid et al [59] presented a data-augmentation framework for generating synthetic time-series training data for an RNN-based deep learning network to accurately and reliably recognize equipment activities. Wang et al [60][61][62] used an augmentation method of jittering and cropping to increase the performance of automatic protective behavior detection. Gao et al [63] presented several data augmentation techniques specifically designed for time-series data in both the time domain and frequency domain.…”

Section: Related Workmentioning

confidence: 99%

Gravity Control-Based Data Augmentation Technique for Improving VR User Activity Recognition

2021

View full text Add to dashboard Cite

The neural-network-based human activity recognition (HAR) technique is being increasingly used for activity recognition in virtual reality (VR) users. The major issue of a such technique is the collection large-scale training datasets which are key for deriving a robust recognition model. However, collecting large-scale data is a costly and time-consuming process. Furthermore, increasing the number of activities to be classified will require a much larger number of training datasets. Since training the model with a sparse dataset can only provide limited features to recognition models, it can cause problems such as overfitting and suboptimal results. In this paper, we present a data augmentation technique named gravity control-based augmentation (GCDA) to alleviate the sparse data problem by generating new training data based on the existing data. The benefits of the symmetrical structure of the data are that it increased the number of data while preserving the properties of the data. The core concept of GCDA is two-fold: (1) decomposing the acceleration data obtained from the inertial measurement unit (IMU) into zero-gravity acceleration and gravitational acceleration, and augmenting them separately, and (2) exploiting gravity as a directional feature and controlling it to augment training datasets. Through the comparative evaluations, we validated that the application of GCDA to training datasets showed a larger improvement in classification accuracy (96.39%) compared to the typical data augmentation methods (92.29%) applied and those that did not apply the augmentation method (85.21%).

show abstract

“…As digital cameras and motion sensors became ubiquitous and are commonly used in mobile phones and office environments, various MSD detection methods have been developed based on commercially available devices [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Along with this, various resources have been created, including datasets made with cameras [13][14][15][16][17][18][19], wearable motion capture sensors [20][21][22][23], Kinect devices [17,[24][25][26][27], and many others.…”

Section: Introductionmentioning

confidence: 99%

MMD-MSD: A Multimodal Multisensory Dataset in Support of Research and Technology Development for Musculoskeletal Disorders

Markova,

Ganchev,

Filkova

et al. 2024

Algorithms

View full text Add to dashboard Cite

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSDs) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support for the prevention of back pain or the development of MSDs. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons, made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal and valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures, and task-specific cognitive load detection from physiological signals.

show abstract

Chronic Pain Protective Behavior Detection with Deep Learning

Cited by 11 publications

References 47 publications

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

Gravity Control-Based Data Augmentation Technique for Improving VR User Activity Recognition

MMD-MSD: A Multimodal Multisensory Dataset in Support of Research and Technology Development for Musculoskeletal Disorders

Contact Info

Product

Resources

About