Passive shoulder occupational exoskeleton reduces shoulder muscle coactivation in repetitive arm movements.

Humans naturally employ muscle coactivation to facilitate a broad range of movements, enhancing joint stability and movement accuracy. However, excessive muscle coactivation can become unfavorable or even detrimental. This phenomenon is often observed in industrial workers who endure repetitive or prolonged joint stress, particularly in areas such as the shoulders.

Evaluation of a spring-loaded upper-limb exoskeleton in cleaning activities.

In the past few years, companies have started considering the adoption of upper-limb occupational exoskeletons as a solution to reduce the health and cost issues associated with work-related shoulder overuse injuries. Most of the previous research studies have evaluated the efficacy of these devices in laboratories by measuring the reduction in muscle exertion resulting from device use in stereotyped tasks and controlled conditions. However, to date, uncertainties exist about generalizing laboratory results to more realistic conditions of use.

Exoskeletons for workers: A case series study in an enclosures production line.

This case-series study aims to investigate the effects of a passive shoulder support exoskeleton on experienced workers during their regular work shifts in an enclosures production site. Experimental activities included three sessions, two of which were conducted in-field (namely, at two workstations of the painting line, where panels were mounted and dismounted from the line; each session involved three participants), and one session was carried out in a realistic simulated environment (namely, the workstations were recreated in a laboratory; this session involved four participants). The effect of the exoskeleton was evaluated through electromyographic activity and perceived effort.

Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing.

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation.

Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters.

Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased.

Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All.

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors.

Locomotory behaviour of the intertidal marble crab (Pachygrapsus marmoratus) supports the underwater spring-loaded inverted pendulum as a fundamental model for punting in animals.

In aquatic pedestrian locomotion the dynamics of terrestrial and aquatic environments are coupled. Here we study terrestrial running and aquatic punting locomotion of the marine-living crab Pachygrapsus marmoratus. We detected both active and passive phases of running and punting through the observation of crab locomotory behaviour in standardized settings and by three-dimensional kinematic analysis of its dynamic gaits using high-speed video cameras.

Grasp force estimation from the transient EMG using high-density surface recordings.

Objective: Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e.

Tongue Rehabilitation Device for Dysphagic Patients.

Dysphagia refers to difficulty in swallowing often associated with syndromic disorders. In dysphagic patients' rehabilitation, tongue motility is usually treated and monitored via simple exercises, in which the tongue is pushed against a depressor held by the speech therapist in different directions. In this study, we developed and tested a simple pressure/force sensor device, named "Tonic Tongue (ToTo)", intended to support training and monitoring tasks for the rehabilitation of tongue musculature.

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion.

In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network.

Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network.

Loss of stability is a precursor to falling and therefore represents a leading cause of injury, especially in fragile people. Thus, dynamic stability during activities of daily living (ADLs) needs to be considered to assess balance control and fall risk. The dynamic margin of stability (MOS) is often used as an indicator of how the body center of mass is located and moves relative to the base of support.

Reducing Fall Risk with Combined Motor and Cognitive Training in Elderly Fallers.

Background: Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities.

Automatic classification of gait in children with early-onset ataxia or developmental coordination disorder and controls using inertial sensors.

Early-Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two conditions that affect coordination in children. Phenotypic identification of impaired coordination plays an important role in their diagnosis. Gait is one of the tests included in rating scales that can be used to assess motor coordination.

Ambulatory Assessment of Instantaneous Velocity during Walking Using Inertial Sensor Measurements.

A novel approach for estimating the instantaneous velocity of the pelvis during walking was developed based on Inertial Measurement Units (IMUs). The instantaneous velocity was modeled by the sum of a cyclical component, decomposed in the Medio-Lateral (ML), VerTical (VT) and Antero-Posterior (AP) directions, and the Average Progression Velocity (APV) over each gait cycle. The proposed method required the availability of two IMUs, attached to the pelvis and one shank.

Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle.

Purpose: State-of-the-art methods for recognizing human activity using raw data from body-worn accelerometers have primarily been validated with data collected from adults. This study applies a previously available method for activity classification using wrist or ankle accelerometer to data sets collected from both adults and youth.

Methods: An algorithm for detecting activity from wrist-worn accelerometers, originally developed using data from 33 adults, is tested on a data set of 20 youth (age, 13 ± 1.

Dealing with Magnetic Disturbances in Human Motion Capture: A Survey of Techniques.

Magnetic-Inertial Measurement Units (MIMUs) based on microelectromechanical (MEMS) technologies are widespread in contexts such as human motion tracking. Although they present several advantages (lightweight, size, cost), their orientation estimation accuracy might be poor. Indoor magnetic disturbances represent one of the limiting factors for their accuracy, and, therefore, a variety of work was done to characterize and compensate them.

Assessing the Performance of Sensor Fusion Methods: Application to Magnetic-Inertial-Based Human Body Tracking.

Information from complementary and redundant sensors are often combined within sensor fusion algorithms to obtain a single accurate observation of the system at hand. However, measurements from each sensor are characterized by uncertainties. When multiple data are fused, it is often unclear how all these uncertainties interact and influence the overall performance of the sensor fusion algorithm.

A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington's Disease Patients.

Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington's disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist.

Hidden Markov model-based strategy for gait segmentation using inertial sensors: Application to elderly, hemiparetic patients and Huntington's disease patients.

A solution to discriminate stance and swing in both healthy and abnormal gait using inertial sensors is proposed. The method is based on a two states hidden Markov model trained in a supervised way. The proposed method can generalize across different groups of subjects, without the need of parameters tuning.

A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators.

In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators.

Fourier-based integration of quasi-periodic gait accelerations for drift-free displacement estimation using inertial sensors.

Background: In biomechanical studies Optical Motion Capture Systems (OMCS) are considered the gold standard for determining the orientation and the position (pose) of an object in a global reference frame. However, the use of OMCS can be difficult, which has prompted research on alternative sensing technologies, such as body-worn inertial sensors.

Methods: We developed a drift-free method to estimate the three-dimensional (3D) displacement of a body part during cyclical motions using body-worn inertial sensors.

How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy.

In human movement analysis, 3D body segment orientation can be obtained through the numerical integration of gyroscope signals. These signals, however, are affected by errors that, for the case of micro-electro-mechanical systems, are mainly due to: constant bias, scale factor, white noise, and bias instability. The aim of this study is to assess how the orientation estimation accuracy is affected by each of these disturbances, and whether it is influenced by the angular velocity magnitude and 3D distribution across the gyroscope axes.

Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements.

This paper investigates a fall detection system based on the integration of an inertial measurement unit with a barometric altimeter (BIMU). The vertical motion of the body part the BIMU was attached to was monitored on-line using a method that delivered drift-free estimates of the vertical velocity and estimates of the height change from the floor. The experimental study included activities of daily living of seven types and falls of five types, simulated by a cohort of 25 young healthy adults.

Accelerometry-based Recognition of the Placement Sites of a Wearable Sensor.

This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned).

A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer.

Goal: Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion.

Methods: The estimation of the body attitude (i.e.