Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids

Lins, Christian; Eckhoff, Daniel; Klausen, Andreas; Hellmers, Sandra; Hein, Andreas; Fudickar, Sebastian

doi:10.1016/j.asoc.2019.03.023

Cited by 13 publications

(15 citation statements)

References 37 publications

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“…In the aspect of comparing the time efficiency of single fitting cosine curve model, we use the same time window and the same set of data to compare the difference in time efficiency of different algorithms under the condition that RESKIN, PSO, 24 GWO 25 and our proposed algorithms reach the same fitness value. The experimental results are shown in Figure 7.…”

Section: Methodsmentioning

confidence: 99%

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A novel algorithm of fast CPR quality evaluation based on kinect

Xie

Luo

Jie

et al. 2020

Journal of Algorithms & Computational Technology

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Human action recognition is a very popular field in computer vision research, and the research results are widely used in people's lives. This paper explores Kinect-based algorithm of human action recognition and applies it to the quality evaluation of cardiopulmonary resuscitation (CPR) operation. At present, the main means of CRP training is through physical auxiliary equipment, which has a large limitation and can only be carried out under specific conditions. CPR simulation training under general conditions can be effectively carried out by means of computer vision, which is a strategy worth popularizing. Using Kinect's powerful skeleton tracking capabilities to obtain key human skeleton data and then perform fine-grained human action analysis. Our model can obtain the critical compression depth (CCD) and compression frequency (CCF) of CPR. Compared with the-state-of-the-art, our algorithm has better stability and real-time performance. At the same time, our algorithm improves the time efficiency by about 60% while guaranteeing high accuracy. In addition, we guide the human body to perform standard movements by setting joint angle specifications.Moreover, our system has been proven to be valid by professional medical staff.

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Section: Methodsmentioning

confidence: 99%

“…Christian et al. 25 put forward a system called RESKIN. This system uses the skeleton tracking function of Kinect to obtain the skeleton data of human body in real time.…”

Section: Related Workmentioning

confidence: 99%

A novel algorithm of fast CPR quality evaluation based on kinect

Xie

Luo

Jie

et al. 2020

Journal of Algorithms & Computational Technology

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“…While LSTMs are generally suitable for detecting mistakes such as the correct body positions and the locking of the arms, they are not optimal for discovering regularities such as the correct classRate. An alternative method has been used by Lins et al (2019) who fitted a sinusoids function using differential evolution, leveraging the rhythmic nature of the CPR movement. Future model architecture improvements can consider alternative neural network architectures, such as Convolutional Neural Networks or Multivariate Hidden Markov Models tuned for supervised classification.…”

Section: Future Workmentioning

confidence: 99%

Keep Me in the Loop: Real-Time Feedback with Multimodal Data

Mitri

Schneider

Drachsler

2021

Int J Artif Intell Educ

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This paper describes the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects training mistakes using recurrent neural networks. The CPR Tutor automatically recognises and assesses the quality of the chest compressions according to five CPR performance indicators. It detects training mistakes in real-time by analysing a multimodal data stream consisting of kinematic and electromyographic data. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. The mistake detection models of the CPR Tutor were trained using a dataset from 10 experts. Hence, we tested the validity of the CPR Tutor and the impact of its feedback functionality in a user study involving additional 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: (1) an architecture design, (2) a methodological approach for delivering real-time feedback using multimodal data and (3) a field study on real-time feedback for CPR training. This paper details the results of a field study by quantitatively measuring the impact of the CPR Tutor feedback on the performance indicators and qualitatively analysing the participants’ questionnaire answers.

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“…2), due to its implicit periodic accordance with the CPR. A concept that was shown for depthimage based motion capture of CPR movements [21] but still needs to be confirmed for use with acceleration data recorded directly via IMUs at rescuers. Furthermore, the discussed algorithms have been mainly evaluated for the use of IMUs in a grasp-in-hand use (i.e., the sensor or smartphone is held between the back of the lower hand and the palm of the upper hand during the procedure).…”

Section: Introductionmentioning

confidence: 99%

“…To fit the accelerometer data to the sine curve of the model, an appropriate optimization algorithm is required. Evolutionary methods have already been found to be principally suitable for a similar approach: Lins et al [21] Nature-inspired algorithms such as a Genetic Algorithm (GA) have also already proven useful in signal processing [22][23][24]. In addition, there is already some work that has used GAs for curve fitting, e.g.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor

et al. 2021

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Cardiopulmonary resuscitation (CPR) is one of the most critical emergency interventions for sudden cardiac arrest. In this paper, a robust sinusoidal model-fitting method based on a Evolution Strategy inspired algorithm for CPR quality parameters – naming chest compression frequency and depth – as measured by an inertial measurement unit (IMU) attached to the wrist is presented. The proposed approach will allow bystanders to improve CPR as part of a continuous closed-loop support system once integrated into a smartphone or smartwatch application. By evaluating the model’s precision with data recorded by a training mannequin as reference standard, a variance for the compression frequency of $$\pm 2.22$$ ± 2.22 compressions per minute (cpm) has been found for the IMU attached to the wrist. It was found that this previously unconsidered position and thus, the use of smartwatches is a suitable alternative to the typical placement of phones in hand for CPR training.

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Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids

Cited by 13 publications

References 37 publications

A novel algorithm of fast CPR quality evaluation based on kinect

A novel algorithm of fast CPR quality evaluation based on kinect

Keep Me in the Loop: Real-Time Feedback with Multimodal Data

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor

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