Chemotherapy-induced fatigue estimation using hidden Markov model

Ameli, Sina; Naghdy, Fazel; Stirling, David; Naghdy, Golshah; Aghmesheh, Morteza

doi:10.1016/j.bbe.2018.11.002

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…Eight papers did not report the software used (28.6%, [37,39,40,48,53,56,60,64]), 4 papers used custom software (14.3%, [47,49,50,57]). The remaining 6 papers (21.4%, [42,43,52,54,55,61]) reported the use of other software.…”

Section: Parameters Extracted During the 6mwtmentioning

confidence: 99%

“…Ratio between the length of the real path of the foot in 3D space (including both stride length and width) and stride length of one cycle (% stride length) [39] Strike angle Kinematics Angle between the foot and the ground at heel strike in sagittal plane (deg) [39] Lift off angle Kinematics Angle between the foot and the ground at toe off in sagittal plane (deg) [39] Max angular velocity Kinematics Maximum pitch foot angular velocity during swing phase (deg/s) [39] Hip, knee, ankle joint, and pelvis angles Kinematics Joint angles using Euler angle decomposition (deg) [41,45,54,58] Causality index Other Sum of the number of significant relationships remaining after thresholding the pairwise causality matrix [56] Kernel density estimation (KDE) peak Other Peak of the density functions at 100 equally spaced inertial gait data amplitude values [56] Dynamic time warping (DTW) score Other Summarizes the degree of similarity between sequences following alignment [56] Warp score Other Summarizes the number of "warps", or repetitions of samples, needed to achieve an optimal alignment between sequences. [56,62] Change in acceleration pattern between two conditions (δM) Other Difference between two attractors [40] Change in variability around the attractor (δD) Other Change in acceleration variability between conditions [40] Attractor-based index Other Product of δM and δD [40]…”

Section: Path Length Kinematicsmentioning

confidence: 99%

See 1 more Smart Citation

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Storm

Cesareo

Reni

et al. 2020

Sensors

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Wearable sensors are becoming increasingly popular for complementing classical clinical assessments of gait deficits. The aim of this review is to examine the existing knowledge by systematically reviewing a large number of papers focusing on the use of wearable inertial sensors for the assessment of gait during the 6-minute walk test (6MWT), a widely recognized, simple, non-invasive, low-cost and reproducible exercise test. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 28 full-text articles. Then, the available knowledge was summarized regarding study design, subjects enrolled (number of patients and pathological condition, if any, age, male/female ratio), sensor characteristics (type, number, sampling frequency, range) and body placement, 6MWT protocol and extracted parameters. Results were critically discussed to suggest future directions for the use of inertial sensor devices in the clinics.

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Section: Parameters Extracted During the 6mwtmentioning

confidence: 99%

Section: Path Length Kinematicsmentioning

confidence: 99%

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Storm

Cesareo

Reni

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In the G-O model, the software fault function and fault detection rate are fixed values, which simplifies the SRGM parameters and solves the problem that too many parameters are not conducive to parameter optimization. G-O model has a reasonable description of the software failure process and is in line with the actual situation, so it has become a model often used by testers [3].…”

Section: Introductionmentioning

confidence: 99%

Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

Yang

Wang

et al. 2021

IEEE Access

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Software defects reflect software quality, and software failures can be predicted through software reliability models. Aiming at the problem that the parameters of software reliability model are difficult to estimate, this paper used the hybrid algorithm for model parameter estimation to software defect prediction. As a typical swarm intelligence algorithm, PSO (Particle Swarm Optimization) has fast convergence but low solution accuracy. SSA (Sparrow Search Algorithm) not only has high search accuracy and fast convergence speed, but also has the advantages of good stability and strong robustness. Based on the characteristic that the fitness function proposed in this paper, this paper hybrid PSO and SSA to accelerate the convergence before the individual update of the SSA. At the same time, this paper also constructed a new fitness function based on the maximum likelihood estimation of the parameters, and used it for parameter initialization. Through the analysis of the experimental results of five sets of actual data sets, the optimization performance of the hybrid algorithm (SSA-PSO) was better than that of a single algorithm with higher convergence speed and more stable, accurate results. Moreover, with the support of the new fitness function, it effectively solved the problems of slow convergence speed and low accuracy of solution. The experimental results showed that the hybrid SSA-PSO could obtain the better solution, convergence speed and stability than single SSA and PSO in software defections estimation and prediction.

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“…Software reliability is a qualitative indicator for measuring software quality and has important research significance, so it is getting more and more attention from researchers. So far, researchers have put forward nearly a hundred software reliability models, such as GO model [1], MO model [2] and JM model [3], and so on. However, these models are nonlinear function models, and it is difficult to directly estimate their parameters.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Yang

Wang

et al. 2020

IEEE Access

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Software reliability is estimated and predicted based on software reliability model and software failure data. As a new optimization method, swarm intelligence algorithm has been widely used in solving the parameter optimization of the model. WPA (Wolf Pack Algorithm) and PSO (Particle Swarm Optimization) are two typical swarm intelligence algorithms. WPA has a strong global optimization ability, fast convergence speed and various optimization strategies, but the algorithm is relatively complex. PSO algorithm has a simple structure and fast convergence speed, but it is easy to fall into premature, which leads to low accuracy of solution. Considering the advantages and disadvantages of the two algorithms, a hybrid method of WPA and PSO is proposed, and a fitness function is constructed on maximum likelihood estimation, then the parameters of software reliability model are estimated and predicted based on the hybrid algorithm (WPA-PSO). Five sets of data from industry are used to estimate the parameters of GO model and make predictions. The simulation results show that the hybrid algorithm has higher accuracy of parameter estimation, better optimization performance, better accuracy of prediction and algorithm stability than single algorithm, and show obvious advantages than the single algorithm in the case of limited data.

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Chemotherapy-induced fatigue estimation using hidden Markov model

Cited by 3 publications

References 31 publications

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review

Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

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