Publications by authors named "Ranavolo Alberto"

Work-related musculoskeletal disorders (WRMSDs) are a leading cause of chronic conditions among working-age adults. Preventing these disorders is crucial to reducing their impact, and quantitative analysis through sensors can help identify their causes and guide ergonomic solutions. This systematic review aims to compile research from 2000 to 2023 published in English and sourced from Web of Science, Scopus, or PubMed that examines workers' movements during tasks using wearable sensor systems that are applicable in workplace settings.

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Spinal cord injury (SCI) causes major challenges to mobility and daily life activities and maintaining balance becomes a crucial issue. Individuals with SCI often need to adopt new strategies to manage balance with minimal discomfort. Sports and physical activities have become one of the most popular rehabilitation methods for people with SCI.

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Article Synopsis
  • Elite speed roller skaters find skating more physically demanding on the groin compared to other sports, which raises their injury risk.
  • Researchers studied the kinematic and electromyographic data of roller skaters on a treadmill at speeds of 20 km/h and 32 km/h, using sensors to monitor muscle activity.
  • Results showed that higher speeds require greater muscle activation and coordination, providing valuable insights for physiotherapists and kinesiologists in injury prevention and training.
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  • Gait analysis studies in patients with primary hereditary cerebellar ataxia (pwCA) face challenges due to small sample sizes and unbalanced datasets.
  • The study aimed to evaluate how data balancing techniques and generative AI can create synthetic datasets that accurately reflect gait abnormalities in pwCA.
  • Using methods like conditional tabular generative adversarial networks (ctGAN), researchers found that ctGAN improved classification performance and provided better explainability for diagnostic models compared to traditional methods.
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Multiple myeloma (MM) patients complain of pain and stiffness limiting motility. To determine if patients can benefit from vertebroplasty, we assessed muscle activation and co-activation before and after surgery. Five patients with MM and five healthy controls performed sitting-to-standing and lifting tasks.

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The central nervous system (CNS) controls movements and regulates joint stiffness with muscle co-activation, but until now, few studies have examined muscle pairs during running. This study aims to investigate differences in lower limb muscle coactivation during gait at different speeds, from walking to running. Nineteen healthy runners walked and ran at speeds ranging from 0.

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When performing lifting tasks at work, the Lifting Index () is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive () that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying computation and compare calculations in real-time using wearable sensors and force platforms with the estimated with the standard method used by ergonomists and occupational health and safety technicians.

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This study aimed to assess the responsiveness to the rehabilitation of three trunk acceleration-derived gait indexes, namely the harmonic ratio (HR), the short-term longest Lyapunov's exponent (sLLE), and the step-to-step coefficient of variation (CV), in a sample of subjects with primary degenerative cerebellar ataxia (swCA), and investigate the correlations between their improvements (∆), clinical characteristics, and spatio-temporal and kinematic gait features. The trunk acceleration patterns in the antero-posterior (AP), medio-lateral (ML), and vertical (V) directions during gait of 21 swCA were recorded using a magneto-inertial measurement unit placed at the lower back before (T0) and after (T1) a period of inpatient rehabilitation. For comparison, a sample of 21 age- and gait speed-matched healthy subjects (HS) was also included.

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Low back pain (LBP) is a leading cause of disability in the workplace, often caused by manually lifting of heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk of lifting activities. This study aims to verify that, during the execution of fatiguing frequency-dependent lifting, high-density surface electromyography (HDsEMG) allows the discrimination of healthy controls (HC) versus people with LBP and biomechanical risk levels.

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Back support soft exosuits are promising solutions to reduce risk of musculoskeletal injuries at workplaces resulting from physically demanding and repetitive lifting tasks. Design of novel active exosuits address the impact on the muscle activity and metabolic costs but do not consider other critical aspects such as comfort and user perception during the intended tasks. Thus, in this study, we describe a novel soft active exosuit in line with its impact on physiological and subjective measures during lifting.

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Collaborative Robots-CoBots-are emerging as a promising technological aid for workers. To date, most CoBots merely share their workspace or collaborate without contact, with their human partners. We claim that robots would be much more beneficial if they physically collaborated with the worker, on high payload tasks.

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The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors () 1-6.

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Article Synopsis
  • Understanding how the limbs behave during prosthetic gait is crucial for enhancing prosthetic devices and improving mobility for trans-femoral amputees.
  • The study introduces a planar covariation law to effectively describe the gait patterns of amputees using various prosthetic knees, comparing them to control subjects.
  • Findings indicate that while amputees maintain similar gait patterns, notable differences exist in the coordination of their sound side, with potential implications for developing better prosthetic control systems based on kinematic measurements.
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This study aims at evaluating upper limb muscle coordination and activation in workers performing an actual use-case manual material handling (MMH). The study relies on the comparison of the workers' muscular activity while they perform the task, with and without the help of a dual-arm cobot (BAZAR). Eleven participants performed the task and the flexors and extensors muscles of the shoulder, elbow, wrist, and trunk joints were recorded using bipolar electromyography.

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Prosthetic gait implies the use of compensatory motor strategies, including alterations in gait biomechanics and adaptations in the neural control mechanisms adopted by the central nervous system. Despite the constant technological advancements in prostheses design that led to a reduction in compensatory movements and an increased acceptance by the users, a deep comprehension of the numerous factors that influence prosthetic gait is still needed. The quantitative prosthetic gait analysis is an essential step in the development of new and ergonomic devices and to optimize the rehabilitation therapies.

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Individuals of working age affected by neuromuscular disorders frequently experience issues with their capacity to get employment, difficulty at work, and premature work interruption. Anyway, individuals with a disability could be able to return to work, thanks to targeted rehabilitation as well as ergonomic and training interventions. Biomechanical and physiological indexes are important for evaluating motor and muscle performance and determining the success of job integration initiatives.

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Lifting tasks, among manual material handling activities, are those mainly associated with low back pain. In recent years, several instrumental-based tools were developed to quantitatively assess the biomechanical risk during lifting activities. In this study, parameters related to balance and extracted from the Centre of Pressure (CoP) data series are studied in fatiguing frequency-dependent lifting activities to: i) explore the possibility of classifying people with LBP and asymptomatic people during the execution of task; ii) examine the assessment of the risk levels associated with repetitive lifting activities, iii) enhance current understanding of postural control strategies during lifting tasks.

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The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson's disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors.

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Article Synopsis
  • The cerebellum plays a crucial role in regulating human gait, affecting posture, timing of muscle activity, and coordination, with deficits leading to conditions like ataxia and increased fall risk.
  • Cerebellar disorders can disrupt balance and gait due to neurodegeneration, with technologies like accelerometers and Kinect being utilized for assessing gait and developing deep learning methods for better diagnosis.
  • Interventions such as coordinative training are being explored to improve locomotor adaptation in individuals with cerebellar conditions.
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Lifting tasks are manual material-handling activities and are commonly associated with work-related low back disorders. Instrument-based assessment tools are used to quantitatively assess the biomechanical risk associated with lifting activities. This study aims at highlighting different motor strategies in people with and without low back pain (LBP) during fatiguing frequency-dependent lifting tasks by using parameters of muscle coactivation.

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This study aimed to assess the ability of 25 gait indices to characterize gait instability and recurrent fallers among persons with primary degenerative cerebellar ataxia (pwCA), regardless of gait speed, and investigate their correlation with clinical and kinematic variables. Trunk acceleration patterns were acquired during the gait of 34 pwCA, and 34 age- and speed-matched healthy subjects (HS) using an inertial measurement unit. We calculated harmonic ratios (HR), percent recurrence, percent determinism, step length coefficient of variation, short-time largest Lyapunov exponent (sLLE), normalized jerk score, log-dimensionless jerk (LDLJ-A), root mean square (RMS), and root mean square ratio of accelerations (RMSR) in each spatial direction for each participant.

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The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson's disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. The trunk acceleration patterns were acquired during the gait of 55 pwPD and 55 age-and-speed matched healthy subjects using an inertial measurement unit. We calculated the harmonic ratios (HR), percent recurrence, and percent determinism (RQAdet), coefficient of variation, normalized jerk score, and the largest Lyapunov exponent for each participant.

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Workers often develop low back pain due to manually lifting heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk in lifting activities. This study aims to verify the hypothesis that high-density surface electromyography (HDsEMG) allows an optimized discrimination of risk levels associated with different fatiguing lifting conditions compared to traditional bipolar sEMG.

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