Center of Mass Estimation for Rehabilitation in a Multi-contact Environment: A Simulation Study

González, Alejandro H.; Hayashibe, Mitsuhiro; Demircan, Emel; Fraisse, Philippe

doi:10.1109/smc.2013.803

Cited by 6 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of these postures may be too physically demanding for certain patients. To address this, we have also studied the SESC identification for individuals with restricted mobility [ 38 ] and considered multiple support surfaces where CoM can be estimated within the patient's range of motion. This method requires only a variety of postures covering the patient's range of available motion.…”

Section: Discussionmentioning

confidence: 99%

Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

González

Hayashibe

Bonnet

et al. 2014

Sensors

Self Cite

View full text Add to dashboard Cite

The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of physical rehabilitation. This paper develops a method that enables tracking CoM position using low-cost sensors that can be moved around by a therapist or easily installed inside a patient's home. Here, we compare the accuracy of a personalized CoM estimation using the statically equivalent serial chain (SESC) method and measurements obtained with the Kinect to the case of a SESC obtained with high-end equipment (Vicon). We also compare these estimates to literature-based ones for both sensors. The method was validated with seven able-bodied volunteers for whom the SESC was identified using 40 static postures. The literature-based estimation with Vicon measurements had a average error 24.9 ± 3.7 mm; this error was reduced to 12.8 ± 9.1 mm with the SESC identification. When using Kinect measurements, the literature-based estimate had an error of 118.4 ± 50.0 mm, while the SESC error was 26.6 ± 6.0 mm. The subject-specific SESC estimate using low-cost sensors has an equivalent performance as the literature-based one with high-end sensors. The SESC method can improve CoM estimation of elderly and neurologically impaired subjects by considering variations in their mass distribution.

show abstract

Section: Discussionmentioning

confidence: 99%

Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

González

Hayashibe

Bonnet

et al. 2014

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Once it has been identified and trained in the off-line phase, the model can be then employed in the algorithm to calculate the overloading joint torques in the on-line phase, as mentioned in the previous section. Firstly, we identify the whole-body CoM model using the SESC technique, which is based on a reduced complexity dynamic modelling of the human subject and achieves an accuracy of the CoM estimation comparable to the accuracy reached by the classical models that are based on articulated chains [14], [25]. Since the SESC technique is a geometry-based reconstruction of the CoM, the observability of the CoM position is complete [26] and due to the principled simplification approach, the real-time compatibility is addressed [13].…”

Section: Synergistic Modelmentioning

confidence: 99%

A Synergistic Approach to the Real-Time Estimation of the Feet Ground Reaction Forces and Centers of Pressure in Humans With Application to Human–Robot Collaboration

Lorenzini

Kim

Momi

et al. 2018

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

This letter proposes a novel technique for the realtime estimation of the human feet ground reaction forces (GRFs) and centers of pressure (CoP) from the whole-body CoP and body configuration. The estimated variables are used for the estimation of the overloading torques in human joints during double support, with the aim to provide an evaluation of the human ergonomics while performing heavy manipulation tasks with a robot, or when exposed to an external force. The estimation of the feet GRFs and CoP is achieved using a synergistic approach that combines a simplified geometrical model of the whole-body CoP and a learning technique to address the underdetermined force distribution problem. First, a statically equivalent serial chain (SESC) model which enables the whole-body CoP estimation is identified. Then, the estimated whole-body CoP and the simplified body pose information are used for the training and validation of the learning technique. The displacements between the feet CoP estimated using the synergistic model and the values measured using insole sensors, along with the GRFs vectors are finally used for the estimation of the body overloading joint torques. The proposed synergistic model is first validated experimentally in five subjects. Next, its real-time efficacy is assessed in a human-robot load sharing task, in which the robot trajectories are optimized to minimize the effect of the external load on the human joints. Index Terms-Human-centered robotics, human factors and human-in-the-loop, physical human-robot interaction. I. INTRODUCTION I N THE last few years, the great potential and the countless benefits of human-robot collaboration (HRC) are becoming increasingly evident among the scientific and industrial communities. The robots can reduce fatigue and stress by Manuscript

show abstract

“…If the musculoskeletal model of the targeted body area is already validated, it is then possible to estimate muscle forces from the kinematics and the EMG through inverse dynamics [6]. Center of mass can be evaluated without external force sensors [26].…”

Section: B External Force Sensors and Original Techniquesmentioning

confidence: 99%

From on-body sensors to in-body data for health monitoring and medical robotics: A survey

Hernández

Raison

Alexandre

et al. 2014

2014 Global Information Infrastructure and Networking Symposium (GIIS)

View full text Add to dashboard Cite

The estimation of musculoskeletal data such as muscle forces and joint torques could have a significant impact on patient care monitoring and medical robotics as well as on reducing healthcare and industrial costs by improving the treatment in the field of rehabilitation. Direct measurement of these data is now non-invasive, as they are computed from dedicated wireless on-body sensors, which can synchronously measure segment positions, muscle activation, external forces and allow to estimate muscle force and joint torques using musculoskeletal models. This paper presents a state-of-the-art survey reviewing both the most commonly used on-body sensors, over the last thirty years, to compute in-body data and the most popular optokinetic cameras. The results are presented and classified into tables which show the evolution of on-body sensors since the 1980's, but also the challenges that lie ahead, as very accurate sensors only accentuate the faults of an inaccurate musculoskeletal model. The survey results show that there is a lack of studies validating the different musculoskeletal models. In addition, current interfaces between hardware and software could be improved.

show abstract

Center of Mass Estimation for Rehabilitation in a Multi-contact Environment: A Simulation Study

Cited by 6 publications

References 18 publications

Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

A Synergistic Approach to the Real-Time Estimation of the Feet Ground Reaction Forces and Centers of Pressure in Humans With Application to Human–Robot Collaboration

From on-body sensors to in-body data for health monitoring and medical robotics: A survey

Contact Info

Product

Resources

About