Fatigue Detection during Sit-To-Stand Test Based on Surface Electromyography and Acceleration: A Case Study

AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets

BACKGROUND

Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes' physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina.

MATERIALS AND METHODS

IMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation.

RESULTS

The AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes' physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance.

CONCLUSIONS

In sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model's efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.

An Evaluation of an Innovative Exercise to Relieve Chronic Low Back Pain in Sedentary Workers

OBJECTIVE

The purpose of the study was to examine the effectiveness of a novel supported dynamic lumbar extension with the abdominal drawing-in maneuver (ADIM) technique on stature change, deep abdominal muscle activity, trunk muscle fatigue, and pain intensity during prolonged sitting in chronic low back pain (CLBP) participants.

BACKGROUND

Prolonged sitting can cause trunk muscle fatigue from continuous contraction of deep trunk muscles in seated postures. Deficiency of activity of deep muscles can reduce muscular support of the spine, causing stress on spinal structures, which could result in pain.

METHOD

Thirty participants with CLBP were randomly allocated: (a) control-sitting without exercise, and (b) intervention-supported dynamic lumbar extension with the ADIM technique.

RESULTS

Compared to the intervention condition, the control condition demonstrated significantly greater deterioration in stature change, increased levels of deep trunk muscle fatigue, and an increase in pain during prolonged sitting.

CONCLUSION

The supported dynamic lumbar extension with the ADIM technique appears to provide a protective effect on detrimental stature change and deep trunk muscle fatigue. In addition, it prevented an increase in pain intensity during prolonged sitting in people with CLBP.

APPLICATION

Sedentary behavior harms health, particularly affecting the lower back. Clinicians can use the intervention to induce dynamic lumbar movement, and this exercise can maintain deep trunk muscle activity during prolonged sitting, thereby helping to prevent low back pain (LBP) problems.

Thirty-second sit-to-stand test as an alternative for estimating peak oxygen uptake and 6-min walking distance in women with breast cancer: a cross-sectional study

Purpose

To determine whether the 30-s sit-to-stand (30STS) test can be a valid tool for estimating and stratifying peak oxygen uptake (VO2peak) and 6-min walking distance (6MWD) in women with breast cancer.

Methods

This cross-sectional study uses data from the ONCORE randomized controlled trial, including 120 women aged 18–70 years with early-stage breast cancer under treatment with anthracycline and/or anti-HER2 antibodies. Participant characteristics were collected at baseline and pooled data from functional assessment (30STS test, relative and absolute VO2peak, and 6MWD) were collected at baseline and post-intervention (comprehensive cardio-oncology rehabilitation program vs. usual care). Bivariate correlations and multivariate linear regression analyses were performed to study the relationship between functional test variables.

Results

The number of repetitions in the 30STS test showed (i) a moderate correlation with relative VO2peak (ml/kg/min) (r = 0.419; p < 0.001; n = 126), (ii) a weak correlation with absolute VO2peak (ml/min) (r = 0.241; p = 0.008; n = 120), and (iii) a moderate correlation with the 6MWD (r = 0.440; p < 0.001; n = 85). The ONCORE equations obtained from the multivariate regression models allowed the estimation of VO2peak and 6MWD (r² = 0.390; r² = 0.261, respectively) based on the 30STS test, and its stratification into tertiles (low, moderate, and high).

Conclusion

The 30STS test was found to be a useful tool to estimate VO2peak and/or 6MWD in women with early-stage breast cancer. Its use may facilitate the assessment and stratification of functional capacity in this population for the implementation of therapeutic exercise programs if cardiopulmonary exercise testing (CPET) or 6MWT are not available.

Trial registration

ClinicalTrials.gov Identifier: NCT03964142. Registered on 28 May 2019. Retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT03964142

Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait

The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.

Artificial Intelligence Adoption in the Post COVID-19 New-Normal and Role of Smart Technologies in Transforming Business: a Review

Purpose

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

Design/methodology/approach

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

Findings

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research limitations/implications

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

Practical implications

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

Originality/value

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

The Effect of Core Stabilization Exercise with the Abdominal Drawing-in Maneuver Technique on Stature Change during Prolonged Sitting in Sedentary Workers with Chronic Low Back Pain

To enhance stature recovery, lumbar spine stabilization by stimulating the deep trunk muscle activation for compensation forces originating from the upper body was introduced. The abdominal drawing-in maneuver (ADIM) technique has been found mainly to activate deep trunk muscles. The purpose of the current study was to determine whether 5 weeks of training of deep trunk muscles using the ADIM technique could improve stature recovery, delay trunk muscle fatigue, and decrease pain intensity during prolonged sitting. Thirty participants with chronic low back pain (CLBP) conducted a core stabilization exercise (CSE) with the ADIM technique for 5 weeks. Participants were required to sit for 41 min before and after the exercise intervention. Stature change was measured using a seated stadiometer with a resolution of ±0.006 mm. During sitting, the stature change, pain intensity, and trunk muscle fatigue were recorded. A comparison between measurements at baseline and after 5 weeks of training demonstrated: (i) stature recovery and pain intensity significantly improved throughout the 41 min sitting condition; (ii) the bilaterally trunk muscle showed significantly decreased fatigue. The CSE with the ADIM technique was shown to provide a protective effect on detrimental reductions in stature change and trunk muscle fatigue during prolonged sitting in young participants under controlled conditions in a laboratory. This information may help to prevent the risk of LBP from prolonged sitting activities in real life situations.

Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors

The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.

Development of a Mitochondrial Myopathy-Composite Assessment Tool

BACKGROUND

'Mitochondrial Myopathy' (MM) refers to genetically-confirmed Primary Mitochondrial Disease (PMD) that predominantly impairs skeletal muscle function. Validated outcome measures encompassing core MM domains of muscle weakness, muscle fatigue, imbalance, impaired dexterity, and exercise intolerance do not exist. The goal of this study was to validate clinically-meaningful, quantitative outcome measures specific to MM.

METHODS

This was a single centre study. Objective measures evaluated included hand-held dynamometry, balance assessments, Nine Hole Peg Test (9HPT), Functional Dexterity Test (FDT), 30 second Sit to Stand (30s STS), and 6-minute walk test (6MWT). Results were assessed as z-scores, with < -2 standard deviations considered abnormal. Performance relative to the North Star Ambulatory Assessment (NSAA) of functional mobility was assessed by Pearson's correlation.

RESULTS

In genetically-confirmed MM participants [n = 59, mean age 21.6 ± 13.9 (range 7 - 64.6 years), 44.1% male], with nuclear gene aetiologies, n = 18/59, or mitochondrial (mtDNA) aetiologies, n = 41/59, dynamometry measurements demonstrated both proximal [dominant elbow flexion (-2.6 ± 2.1, mean z-score ± standard deviation, SD), hip flexion (-2.5 ± 2.3), and knee flexion (-2.8 ± 1.3)] and distal muscle weakness [wrist extension (-3.4 ± 1.7), palmar pinch (-2.5 ± 2.8), and ankle dorsiflexion (-2.4 ± 2.5)]. Balance [Tandem Stance (TS) Eyes Open (-3.2 ± 8.8, n = 53) and TS Eyes Closed (-2.6 ± 2.7, n = 52)] and dexterity [FDT (-5.9 ± 6.0, n = 44) and 9HPT (-8.3 ± 11.2, n = 53)] assessments also revealed impairment. Exercise intolerance was confirmed by strength-based 30s STS test (-2.0 ± 0.8, n = 38) and mobility-based 6MWT mean z-score (-2.9 ± 1.3, n = 46) with significant decline in minute distances (slope -0.9, p = 0.03, n = 46). Muscle fatigue was quantified by dynamometry repetitions with strength decrement noted between first and sixth repetitions at dominant elbow flexors (-14.7 ± 2.2%, mean ± standard error, SEM, n = 21). All assessments were incorporated in the MM-Composite Assessment Tool (MM-COAST). MM-COAST composite score for MM participants was 1.3± 0.1(n = 53) with a higher score indicating greater MM disease severity, and correlated to NSAA (r = 0.64, p < 0.0001, n = 52) to indicate clinical meaning. Test-retest reliability of MM-COAST assessments in an MM subset (n = 14) revealed an intraclass correlation coefficient (ICC) of 0.81 (95% confidence interval: 0.59-0.92) indicating good reliability.

CONCLUSIONS

We have developed and successfully validated a MM-specific Composite Assessment Tool to quantify the key domains of MM, shown to be abnormal in a Definite MM cohort. MM-COAST may hold particular utility as a meaningful outcome measure in future MM intervention trials.

Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise

Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients' condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject's physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.

Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements.

Smart technologies driven approaches to tackle COVID-19 pandemic: a review

The novel coronavirus infection (COVID-19) is not diminishing without vaccine, but it impinges on human safety and economy can be minimized by adopting smart technology to combat pandemic situation. The implementation of new innovations and novel tactics has proven to be effective in curbing the risk of COVID-19. The present study covers the role of smart technology in mitigating the spread of COVID-19 with specific focus on advancement in the field of drone, robotics, artificial intelligence (AI), mask, and sensor technology. The findings shed light on the robotics and drone technology-driven approaches that have been applied for assisting health system, surveillance, and disinfection process, etc. The AI technology strategies and framework is highlighted in terms of bulk data computing, predicting infection threats, providing medical assistance, and analyzing diagnosis results. Besides this, the technological shift in mask and sensor technology during the pandemic have been illustrated, which includes fabrication method like 3D printing and optical sensing, respectively. Furthermore, the strength, weakness, opportunities, and possible threats that have been shaped by the rigorous implementation of these technologies are also covered in detail.

Sit-To-Stand Movement Evaluated Using an Inertial Measurement Unit Embedded in Smart Glasses-A Validation Study

Wearable sensors have recently been used to evaluate biomechanical parameters of everyday movements, but few have been located at the head level. This study investigated the relative and absolute reliability (intra- and inter-session) and concurrent validity of an inertial measurement unit (IMU) embedded in smart eyeglasses during sit-to-stand (STS) movements for the measurement of maximal acceleration of the head. Reliability and concurrent validity were investigated in nineteen young and healthy participants by comparing the acceleration values of the glasses’ IMU to an optoelectronic system. Sit-to-stand movements were performed in laboratory conditions using standardized tests. Participants wore the smart glasses and completed two testing sessions with STS movements performed at two speeds (slow and comfortable) under two different conditions (with and without a cervical collar). Both the vertical and anteroposterior acceleration values were collected and analyzed. The use of the cervical collar did not significantly influence the results obtained. The relative reliability intra- and inter-session was good to excellent (i.e., intraclass correlation coefficients were between 0.78 and 0.91) and excellent absolute reliability (i.e., standard error of the measurement lower than 10% of the average test or retest value) was observed for the glasses, especially for the vertical axis. Whatever the testing sessions in all conditions, significant correlations (p < 0.001) were found for the acceleration values recorded either in the vertical axis and in the anteroposterior axis between the glasses and the optoelectronic system. Concurrent validity between the glasses and the optoelectronic system was observed. Our observations indicate that the IMU embedded in smart glasses is accurate to measure vertical acceleration during STS movements. Further studies should investigate the use of these smart glasses to assess the STS movement in unstandardized settings (i.e., clinical and/or home) and to report vertical acceleration values in an elderly population of fallers and non-fallers.