Pub Date : 2025-03-13DOI: 10.1016/j.medengphy.2025.104326
Bing Rao , Ruige Fang , Changchen Zhao , Jie Bai
Remote photoplethysmography (rPPG) has long been an active research topic. Existing rPPG approaches achieve high accuracy of heart rate extraction, as long as the user is relatively close to the camera (typically, less than 1 meter distance). This article investigates the performance of existing rPPG approaches under the long-distance recording conditions and proposes a novel Projection of Rotated Orthogonal Bases in POS (ProPOS) algorithm for heart rate extraction. A set of orthogonal projection bases is generated around the original plain of POS algorithm. The raw measurement traces are projected on these bases and the final output signal is obtained by a designed SNR selection criterion. The long-distance rPPG (LD-rPPG) dataset is established for long-distance rPPG research by varying the recording distance from 3 m-30 m. Extensive experiments are performed in comparison with existing approaches. Experiments show that videos recorded by HikVision DS-V108 and Logitech C920 cameras contain a certain amount of physiological signal whereas the videos recorded by HikVision DS-U102D and Mercury cameras contain little physiological signal. Using zoom lenses is beneficial to improve the rPPG measurement accuracy under long-distance conditions.
{"title":"Measurement of heart rate from long-distance videos via projection of rotated orthogonal bases in POS","authors":"Bing Rao , Ruige Fang , Changchen Zhao , Jie Bai","doi":"10.1016/j.medengphy.2025.104326","DOIUrl":"10.1016/j.medengphy.2025.104326","url":null,"abstract":"<div><div>Remote photoplethysmography (rPPG) has long been an active research topic. Existing rPPG approaches achieve high accuracy of heart rate extraction, as long as the user is relatively close to the camera (typically, less than 1 meter distance). This article investigates the performance of existing rPPG approaches under the long-distance recording conditions and proposes a novel Projection of Rotated Orthogonal Bases in POS (ProPOS) algorithm for heart rate extraction. A set of orthogonal projection bases is generated around the original plain of POS algorithm. The raw measurement traces are projected on these bases and the final output signal is obtained by a designed SNR selection criterion. The long-distance rPPG (LD-rPPG) dataset is established for long-distance rPPG research by varying the recording distance from 3 m-30 m. Extensive experiments are performed in comparison with existing approaches. Experiments show that videos recorded by HikVision DS-V108 and Logitech C920 cameras contain a certain amount of physiological signal whereas the videos recorded by HikVision DS-U102D and Mercury cameras contain little physiological signal. Using zoom lenses is beneficial to improve the rPPG measurement accuracy under long-distance conditions.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104326"},"PeriodicalIF":1.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-10DOI: 10.1016/j.medengphy.2025.104323
Gillian Kane , Clarissa LeVasseur , Ajinkya Rai , Maria Munsch , Alexandra S. Gabrielli , Christopher J. Como , Jonathan D. Hughes , William Anderst , Albert Lin
The purpose of this study was to identify surgical techniques and implant geometries that influence in-vivo kinematics, functional outcomes, and clinical outcomes after reverse shoulder arthroplasty (RSA). Synchronized biplane radiographs imaged the operated shoulder during scapular plane abduction in 35 patients who received RSA within the past 2.5 ± 1.2 yrs. Shoulder kinematics and arthrokinematics (contact paths) were determined by matching subject-specific CT-based bone-plus-implant models to the radiographs using a validated tracking technique. Torque and total work done during abduction were measured using an isokinetic dynamometer. Implant characteristics and surgical techniques that were associated with kinematics/arthrokinematics, strength, or patient-reported outcomes were identified using multiple linear regression. Neck shaft angle, glenosphere size, and retroversion were associated with in-vivo kinematics and functional outcomes during abduction after RSA. These findings improve our understanding of how implant design and surgical technique impact kinematics and functional outcomes after RSA. The results highlight the necessity of in vivo data to validate cadaver-based research and computer simulations of joint function after RSA, emphasizing that those models do not account for the dynamic healing process and neuromuscular adaptations that occur after surgery.
{"title":"Surgical technique and implant design affect abduction kinematics and functional outcomes after reverse shoulder arthroplasty","authors":"Gillian Kane , Clarissa LeVasseur , Ajinkya Rai , Maria Munsch , Alexandra S. Gabrielli , Christopher J. Como , Jonathan D. Hughes , William Anderst , Albert Lin","doi":"10.1016/j.medengphy.2025.104323","DOIUrl":"10.1016/j.medengphy.2025.104323","url":null,"abstract":"<div><div>The purpose of this study was to identify surgical techniques and implant geometries that influence <em>in-vivo</em> kinematics, functional outcomes, and clinical outcomes after reverse shoulder arthroplasty (RSA). Synchronized biplane radiographs imaged the operated shoulder during scapular plane abduction in 35 patients who received RSA within the past 2.5 ± 1.2 yrs. Shoulder kinematics and arthrokinematics (contact paths) were determined by matching subject-specific CT-based bone-plus-implant models to the radiographs using a validated tracking technique. Torque and total work done during abduction were measured using an isokinetic dynamometer. Implant characteristics and surgical techniques that were associated with kinematics/arthrokinematics, strength, or patient-reported outcomes were identified using multiple linear regression. Neck shaft angle, glenosphere size, and retroversion were associated with <em>in-</em>vivo kinematics and functional outcomes during abduction after RSA. These findings improve our understanding of how implant design and surgical technique impact kinematics and functional outcomes after RSA. The results highlight the necessity of <em>in vivo</em> data to validate cadaver-based research and computer simulations of joint function after RSA, emphasizing that those models do not account for the dynamic healing process and neuromuscular adaptations that occur after surgery.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104323"},"PeriodicalIF":1.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predictions of primary fixation in total knee arthroplasty (TKA) can aid in implant design, optimizing long-term survival. Finite element (FE) simulations are commonly used to assess micromotions at the bone-implant interface during daily activities, requiring accurate computational models. A key factor is the material model used to simulate bone properties. This study evaluated two plastic material models—Isotropic Crushable Foam (ICF) and softening Von-Mises (sVM)—for predicting primary fixation in femoral TKA components. Mechanical tests on human femoral trabecular bone samples under cyclic loading were replicated using FE simulations with ICF and sVM models. These models were then applied to FE simulations of three femoral TKA reconstructions, representing patients aged 57, 73, and 90 years. The ICF model replicated the gradual plastic deformation observed in experiments, unlike the sVM model, and more closely matched the permanent deformation of bone samples. In primary fixation simulations, micromotions at the bone-implant interface averaged 27 µm with ICF and 17 µm with sVM. While both predictions fall within acceptable ranges, the ICF model, as a pressure-dependent material model, more accurately replicates experimental energy dissipation and plastic deformation patterns, closely mirroring human bone's plastic behavior. This makes it better suited for simulating implant-bone interface micromotions in primary TKA fixation.
{"title":"The effect of bone plasticity models on simulations of primary fixation in total knee arthroplasty","authors":"Navid Soltanihafshejani , Thom Bitter , Nico Verdonschot , Dennis Janssen","doi":"10.1016/j.medengphy.2025.104329","DOIUrl":"10.1016/j.medengphy.2025.104329","url":null,"abstract":"<div><div>Predictions of primary fixation in total knee arthroplasty (TKA) can aid in implant design, optimizing long-term survival. Finite element (FE) simulations are commonly used to assess micromotions at the bone-implant interface during daily activities, requiring accurate computational models. A key factor is the material model used to simulate bone properties. This study evaluated two plastic material models—Isotropic Crushable Foam (ICF) and softening Von-Mises (sVM)—for predicting primary fixation in femoral TKA components. Mechanical tests on human femoral trabecular bone samples under cyclic loading were replicated using FE simulations with ICF and sVM models. These models were then applied to FE simulations of three femoral TKA reconstructions, representing patients aged 57, 73, and 90 years. The ICF model replicated the gradual plastic deformation observed in experiments, unlike the sVM model, and more closely matched the permanent deformation of bone samples. In primary fixation simulations, micromotions at the bone-implant interface averaged 27 µm with ICF and 17 µm with sVM. While both predictions fall within acceptable ranges, the ICF model, as a pressure-dependent material model, more accurately replicates experimental energy dissipation and plastic deformation patterns, closely mirroring human bone's plastic behavior. This makes it better suited for simulating implant-bone interface micromotions in primary TKA fixation.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104329"},"PeriodicalIF":1.7,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-09DOI: 10.1016/j.medengphy.2025.104321
Nicholas J. Dunbar , Yuhui Zhu , Ata Babazadeh-Naseri , John E. Akin , Benedetta Spazzoli , Claudio Belvedere , Davide M. Donati , Alberto Leardini , Benjamin J. Fregly
Additively manufactured, custom-made implants used for reconstruction are a promising treatment following tumor resection. However, failure rates due to mechanical factors remain high when used in the pelvis for even state-of-the-art prosthesis designs. In a collaborative effort between a clinical and an engineering research team, this study evaluated whether patient-specific biomechanical modeling could predict, in a blinded fashion, the mode and location of a clinically-observed mechanical failure. Multiple failure criteria were considered including the risk of bone fracture due to overloading or stress shielding and prosthesis fracture due to overloading or fatigue. The blinded predictions indicated that the risk of fatigue failure in the pubic screws were eight times higher than the most critical ilium screw and two times higher than the most critical cancellous screw. Simulation of stress-shielding during walking matched evidence of osteolysis in the ilium and pubis. Incorporating patient-specific modeling into the custom implant design process may lead to improved durability.
{"title":"Blinded prediction of custom-made pelvic implant failure using patient-specific finite element modeling","authors":"Nicholas J. Dunbar , Yuhui Zhu , Ata Babazadeh-Naseri , John E. Akin , Benedetta Spazzoli , Claudio Belvedere , Davide M. Donati , Alberto Leardini , Benjamin J. Fregly","doi":"10.1016/j.medengphy.2025.104321","DOIUrl":"10.1016/j.medengphy.2025.104321","url":null,"abstract":"<div><div>Additively manufactured, custom-made implants used for reconstruction are a promising treatment following tumor resection. However, failure rates due to mechanical factors remain high when used in the pelvis for even state-of-the-art prosthesis designs. In a collaborative effort between a clinical and an engineering research team, this study evaluated whether patient-specific biomechanical modeling could predict, in a blinded fashion, the mode and location of a clinically-observed mechanical failure. Multiple failure criteria were considered including the risk of bone fracture due to overloading or stress shielding and prosthesis fracture due to overloading or fatigue. The blinded predictions indicated that the risk of fatigue failure in the pubic screws were eight times higher than the most critical ilium screw and two times higher than the most critical cancellous screw. Simulation of stress-shielding during walking matched evidence of osteolysis in the ilium and pubis. Incorporating patient-specific modeling into the custom implant design process may lead to improved durability.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104321"},"PeriodicalIF":1.7,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-08DOI: 10.1016/j.medengphy.2025.104324
Thomas Gersie , Thom Bitter , David Wolfson , Robert Freeman , Nico Verdonschot , Dennis Janssen
Computational models of orthopedic reconstructions are reliant on bone material properties, but viscoelastic behavior of trabecular bone is often ignored in numerical simulations. The inclusion of stress relaxation could be of importance for the accuracy of models simulating the primary stability of cementless implants. In this study, a material model to describe the nonlinear viscoelastic behavior of human trabecular bone was constructed based on uniaxial stress relaxation experiments. The relationship of bone mineral density (BMD) and stress relaxation was explored, and the material model was implemented in sample-specific finite element (FE) simulations.
Cylindrical trabecular human bone specimens, from the distal femur and proximal tibia, were subjected to stress relaxation tests, undergoing compression with strains from 0.2 % to 0.8 % for 30 min on four consecutive days. The experimental data were extrapolated to 24 h. Similar levels of stress relaxation were found for femoral and tibial specimens, with an average 54.4 % stress relaxation and a maximum level of 81.6 %. Using a modified superposition model, the specimen-specific nonlinear stress relaxation behavior was captured. However, when the samples were considered collectively, no correlation was found between applied strain, BMD and the viscoelastic response. Therefore, the average level of stress relaxation in combination with existing BMD-stiffness relationships were implemented in FE simulations for each individual specimen. While the FE models, on average, overestimated the overall stiffness by 64 %, they were able to adequately capture the stress relaxation response.
{"title":"Characterization of nonlinear stress relaxation of the femoral and tibial trabecular bone for computational modeling","authors":"Thomas Gersie , Thom Bitter , David Wolfson , Robert Freeman , Nico Verdonschot , Dennis Janssen","doi":"10.1016/j.medengphy.2025.104324","DOIUrl":"10.1016/j.medengphy.2025.104324","url":null,"abstract":"<div><div>Computational models of orthopedic reconstructions are reliant on bone material properties, but viscoelastic behavior of trabecular bone is often ignored in numerical simulations. The inclusion of stress relaxation could be of importance for the accuracy of models simulating the primary stability of cementless implants. In this study, a material model to describe the nonlinear viscoelastic behavior of human trabecular bone was constructed based on uniaxial stress relaxation experiments. The relationship of bone mineral density (BMD) and stress relaxation was explored, and the material model was implemented in sample-specific finite element (FE) simulations.</div><div>Cylindrical trabecular human bone specimens, from the distal femur and proximal tibia, were subjected to stress relaxation tests, undergoing compression with strains from 0.2 % to 0.8 % for 30 min on four consecutive days. The experimental data were extrapolated to 24 h. Similar levels of stress relaxation were found for femoral and tibial specimens, with an average 54.4 % stress relaxation and a maximum level of 81.6 %. Using a modified superposition model, the specimen-specific nonlinear stress relaxation behavior was captured. However, when the samples were considered collectively, no correlation was found between applied strain, BMD and the viscoelastic response. Therefore, the average level of stress relaxation in combination with existing BMD-stiffness relationships were implemented in FE simulations for each individual specimen. While the FE models, on average, overestimated the overall stiffness by 64 %, they were able to adequately capture the stress relaxation response.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104324"},"PeriodicalIF":1.7,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A wireless sensor-instrumented femoral head was developed that enables the intraoperative measurement of three force components in a total hip arthroplasty (THA). The design of the head was 32 mm in diameter with a 12/14 taper at the bottom, being compatible with commonly available THA. Inside the femoral head, there are three Hall effect sensors paired with permanent magnets, along with a microprocessor, a Bluetooth wireless module, a noncontact reed switch, and three lithium button batteries, all components were hermetically encapsulated. These components work together to measure and transmit the hip forces applied to the head during surgery. Through bench-top calibration, the system demonstrated a mean measurement error of approximately 8 % for forces up to 800 N. Additionally the practical applications of the prosthesis were tested on three hips from two cadavers undergoing THA. The hip forces were successively measured as the hip was passively moved, revealing insights into the potential for anterior or posterior dislocations with dislocation-prone positions. Understanding these intraoperative hip forces could aid surgeons in making the optimal decisions regarding implant placement and component selection, to minimize the risk of postoperative complications such as dislocation. Overall, this sensor-instrumented prosthesis will be useful for quantifying intraoperative hip forces.
{"title":"A wireless intraoperative joint force sensing system for total hip arthroplasty","authors":"Masaru Higa , Hiromasa Tanino , Hirai Yusuke , Inoue Seita , Mitsutake Ryo , Ito Hiroshi","doi":"10.1016/j.medengphy.2025.104325","DOIUrl":"10.1016/j.medengphy.2025.104325","url":null,"abstract":"<div><div>A wireless sensor-instrumented femoral head was developed that enables the intraoperative measurement of three force components in a total hip arthroplasty (THA). The design of the head was 32 mm in diameter with a 12/14 taper at the bottom, being compatible with commonly available THA. Inside the femoral head, there are three Hall effect sensors paired with permanent magnets, along with a microprocessor, a Bluetooth wireless module, a noncontact reed switch, and three lithium button batteries, all components were hermetically encapsulated. These components work together to measure and transmit the hip forces applied to the head during surgery. Through bench-top calibration, the system demonstrated a mean measurement error of approximately 8 % for forces up to 800 N. Additionally the practical applications of the prosthesis were tested on three hips from two cadavers undergoing THA. The hip forces were successively measured as the hip was passively moved, revealing insights into the potential for anterior or posterior dislocations with dislocation-prone positions. Understanding these intraoperative hip forces could aid surgeons in making the optimal decisions regarding implant placement and component selection, to minimize the risk of postoperative complications such as dislocation. Overall, this sensor-instrumented prosthesis will be useful for quantifying intraoperative hip forces.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104325"},"PeriodicalIF":1.7,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Osteoarthritis is a common cause of disability among elderly significantly affecting their quality of life due to pain and functional limitations. This study proposes a novel, non-invasive, and cost-effective diagnostic technique using vibroarthrography (VAG) for early detection and grading of knee osteoarthritis (KOA) overcoming the limitations of traditional methods like X-rays, CT scans, and MRIs. Signal acquisition involved capturing of VAG signals from KOA patients using Thinklabs One digital stethoscope and a specialized knee brace within a frequency range of 20 Hz to 2000 Hz with a ± 3 dB tolerance at 44,000 samples per second. Various signal processing techniques, like time domain, statistical, PSD, wavelet, and Hilbert-Huang transform analysis, were used to study the resultant signal. Subsequently, a novel combination of self-organizing maps (SOMs) and K-means clustering was proposed to categorize VAG signals into distinct OA grade clusters. The resulting analysis identified distinct patterns in the time domain correlating with joint alteration severity. A SD/Mean ratio differentiated OA grades. Hilbert-Huang Transform established intrinsic mode functions relating frequency bands to OA stages, while wavelet and spectrogram analysis demonstrated increased signal complexity and variability with disease progression. The effectiveness of proposed clustering model was indicated by high mean Silhouette Coefficient (∼0.80) and low Davies-Bouldin Index (∼0.33) indicating distinct and accurate segmentation of OA stages. These findings clearly highlighted the potential of SOMs and K-means clustering in analysing VAG signals for classifying into different KOA grades. These results demonstrate the substantial potential of advanced signal processing, SOMs, and K-means clustering in uncovering complex patterns in VAG data, linking increasing knee sound signal complexity with OA progression. This highlights the potential of our approach in medical diagnostics, especially for chronic conditions like KOA, where early detection and ongoing monitoring are crucial.
{"title":"Advance signal processing and machine learning approach for analysis and classification of knee osteoarthritis vibroarthrographic signals","authors":"Vikas Kumar , Pooja Kumari Jha , Manoj Kumar Parida , Jagannatha Sahoo","doi":"10.1016/j.medengphy.2025.104322","DOIUrl":"10.1016/j.medengphy.2025.104322","url":null,"abstract":"<div><div>Osteoarthritis is a common cause of disability among elderly significantly affecting their quality of life due to pain and functional limitations. This study proposes a novel, non-invasive, and cost-effective diagnostic technique using vibroarthrography (VAG) for early detection and grading of knee osteoarthritis (KOA) overcoming the limitations of traditional methods like X-rays, CT scans, and MRIs. Signal acquisition involved capturing of VAG signals from KOA patients using Thinklabs One digital stethoscope and a specialized knee brace within a frequency range of 20 Hz to 2000 Hz with a ± 3 dB tolerance at 44,000 samples per second. Various signal processing techniques, like time domain, statistical, PSD, wavelet, and Hilbert-Huang transform analysis, were used to study the resultant signal. Subsequently, a novel combination of self-organizing maps (SOMs) and K-means clustering was proposed to categorize VAG signals into distinct OA grade clusters. The resulting analysis identified distinct patterns in the time domain correlating with joint alteration severity. A SD/Mean ratio differentiated OA grades. Hilbert-Huang Transform established intrinsic mode functions relating frequency bands to OA stages, while wavelet and spectrogram analysis demonstrated increased signal complexity and variability with disease progression. The effectiveness of proposed clustering model was indicated by high mean Silhouette Coefficient (∼0.80) and low Davies-Bouldin Index (∼0.33) indicating distinct and accurate segmentation of OA stages. These findings clearly highlighted the potential of SOMs and K-means clustering in analysing VAG signals for classifying into different KOA grades. These results demonstrate the substantial potential of advanced signal processing, SOMs, and K-means clustering in uncovering complex patterns in VAG data, linking increasing knee sound signal complexity with OA progression. This highlights the potential of our approach in medical diagnostics, especially for chronic conditions like KOA, where early detection and ongoing monitoring are crucial.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104322"},"PeriodicalIF":1.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-02DOI: 10.1016/j.medengphy.2025.104316
Avishek Paul , Saurabh Pal , Madhuchhanda Mitra
With the development of neuroscience and computer science, there is a push to employ automated methods to assist individuals in identifying their emotions. Emotion detection is normally carried out by using electroencephalogram (EEG) signals. However, the medical equipment is costly, uncomfortable, and inconvenient because of the numerous electrodes and hair-covered scalp. This challenge demands for a solution to this problem where the requirement of so many electrodes will be replaced by one or two electrodes followed by a simpler signal processing steps. As a solution to this, the current study proposes an algorithm which uses only a pair of EEG electrodes for identifying primary emotions and classifies them based on threshold based rule along with standard classification techniques. The algorithm utilizes two simple features based on signal energy variations in the sub band levels and a feature fusion technique is adopted to further reduce the computational burden. This will lead to reduction in processing power to a greater extent and practical viability will be enhanced. The experimental results prove that the feature fusion strategy does raise recognition accuracy from 97.7 % to 98.4 %. It is shown that the suggested method for emotional recognition is workable and efficient which can be implemented on portable hardware platforms with minimum memory and computational power requirement.
{"title":"A simple algorithm for primary emotion recognition from dual channel EEG signals","authors":"Avishek Paul , Saurabh Pal , Madhuchhanda Mitra","doi":"10.1016/j.medengphy.2025.104316","DOIUrl":"10.1016/j.medengphy.2025.104316","url":null,"abstract":"<div><div>With the development of neuroscience and computer science, there is a push to employ automated methods to assist individuals in identifying their emotions. Emotion detection is normally carried out by using electroencephalogram (EEG) signals. However, the medical equipment is costly, uncomfortable, and inconvenient because of the numerous electrodes and hair-covered scalp. This challenge demands for a solution to this problem where the requirement of so many electrodes will be replaced by one or two electrodes followed by a simpler signal processing steps. As a solution to this, the current study proposes an algorithm which uses only a pair of EEG electrodes for identifying primary emotions and classifies them based on threshold based rule along with standard classification techniques. The algorithm utilizes two simple features based on signal energy variations in the sub band levels and a feature fusion technique is adopted to further reduce the computational burden. This will lead to reduction in processing power to a greater extent and practical viability will be enhanced. The experimental results prove that the feature fusion strategy does raise recognition accuracy from 97.7 % to 98.4 %. It is shown that the suggested method for emotional recognition is workable and efficient which can be implemented on portable hardware platforms with minimum memory and computational power requirement.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104316"},"PeriodicalIF":1.7,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.medengphy.2025.104312
Yang Song , Ziyu Yuan , Yuxin Wu
Accurate monitoring of blood glucose levels and the prediction of hyperglycemia are critical for the management of diabetes and the enhancement of medical efficiency. The primary challenge lies in uncovering the correlations among physiological information, nutritional intake, and other features, and addressing the issue of scale disparity among these features, in addition to considering the impact of individual variances on the model's accuracy. This paper introduces a universal, wearable device-assisted, multi-scale feature fusion model for real-time blood glucose monitoring and hyperglycemia prediction. It aims to more effectively capture the local correlations between diverse features and their inherent temporal relationships, overcoming the challenges of physiological data redundancy at large time scales and the incompleteness of nutritional intake data at smaller time scales. Furthermore, we have devised a personalized tuner strategy to enhance the model's accuracy and stability by continuously collecting personal data from users of the wearable devices to fine-tune the generic model, thereby accommodating individual differences and providing patients with more precise health management services. The model's performance, assessed using public datasets, indicates that the real-time monitoring error in terms of Mean Squared Error (MSE) is 0.22mmol/L, with a prediction accuracy for hyperglycemia occurrences of 96.75%. The implementation of the personalized tuner strategy yielded an average improvement rate of 1.96% on individual user datasets. This study on blood glucose monitoring and hyperglycemia prediction, facilitated by wearable devices, assists users in better managing their blood sugar levels and holds significant clinical application prospects.
{"title":"Multi-scale feature fusion model for real-time Blood glucose monitoring and hyperglycemia prediction based on wearable devices","authors":"Yang Song , Ziyu Yuan , Yuxin Wu","doi":"10.1016/j.medengphy.2025.104312","DOIUrl":"10.1016/j.medengphy.2025.104312","url":null,"abstract":"<div><div>Accurate monitoring of blood glucose levels and the prediction of hyperglycemia are critical for the management of diabetes and the enhancement of medical efficiency. The primary challenge lies in uncovering the correlations among physiological information, nutritional intake, and other features, and addressing the issue of scale disparity among these features, in addition to considering the impact of individual variances on the model's accuracy. This paper introduces a universal, wearable device-assisted, multi-scale feature fusion model for real-time blood glucose monitoring and hyperglycemia prediction. It aims to more effectively capture the local correlations between diverse features and their inherent temporal relationships, overcoming the challenges of physiological data redundancy at large time scales and the incompleteness of nutritional intake data at smaller time scales. Furthermore, we have devised a personalized tuner strategy to enhance the model's accuracy and stability by continuously collecting personal data from users of the wearable devices to fine-tune the generic model, thereby accommodating individual differences and providing patients with more precise health management services. The model's performance, assessed using public datasets, indicates that the real-time monitoring error in terms of Mean Squared Error (MSE) is 0.22mmol/L, with a prediction accuracy for hyperglycemia occurrences of 96.75%. The implementation of the personalized tuner strategy yielded an average improvement rate of 1.96% on individual user datasets. This study on blood glucose monitoring and hyperglycemia prediction, facilitated by wearable devices, assists users in better managing their blood sugar levels and holds significant clinical application prospects.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"138 ","pages":"Article 104312"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.medengphy.2025.104310
Eric D. Thorhauer , Corey Wukelic , Will Lin , Nick Entress , Aerie Grantham , William R. Ledoux
Biplanar fluoroscopy is a powerful, maturing technique for providing clinicians and biomechanists with in vivo kinematic data of the human skeleton during a variety of tasks. Marker-based tracking with biplane systems has applications in both the in vivo and in vitro realms and serves as the established means of validating model-based tracking algorithms. We have developed a custom biplane system for dynamic imaging of the entire foot and ankle complex during gait as well as a custom software suite to perform the required data preprocessing and marker-based tracking. We demonstrate our ability to repeatably model the biplane imaging chains and then accurately and precisely reconstruct the positions of markers in the foot during static and dynamic motion trials. Finally, we simulate the effects of marker localization errors in reconstructing the poses of the calcaneus, navicular, and proximal phalanx during gait in order to contextualize the extent to which marker-based tracking may be considered ground truth compared to future model-based tracking algorithms.
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