Starting date: 2019
Duration: 20 months

 
Project category

OPTION 1: develop a benchmarking solution for one specific benchmarking scenario including one or more outcomes: testbed, software routines and/or experimental datasets.

 
Application

Wearable robots & Humanoids

 
Outcome

Testbed, Sofware & Dataset

 
Team:

COORDINATOR:

  • University of Stuttgart (Germany)

Partner 2:

  • University of Roma Tre (Italy)

Partner 3:

  • University College Dublin (Ireland)
Abstract
Sit-to-stand (STS) is an important task for assessing dynamic balance and lower limb coordination and has been shown to have a significant clinical relevance. As a result, STS is important also for assessing the performance of wearable lower-limb exoskeletons for assistance and humanoid robots that imitate human movements. BENCH aims at developing one integrated system for the assessment and benchmarking of the sit-to-stand gesture in intact and impaired individuals, in human/exoskeleton systems and in humanoid robots. The proposal targets OPTION 1 and aims at developing an instrumented testbed, as well as a set of software routines for the extraction of meaningful neuro-mechanical indicators, together with an initial database of human subjects in different scenarios.
 
BENCH will represent the first implementation of an integrated technical solution for evaluating contact forces, kinematics, kinetics and neuromuscular correlates of sit-to-stand through the integration of fixed (force plates and force sensors) and wearable (IMUs) sensors. The testbed will mainly consist of an instrumented chair/ground apparatus with two 6-DoF force plates for the measurement of interaction forces, instrumented handrails and a set of body-worn IMUs. All the hardware integrates commercially available instrumentation. The whole system/testbed will be ready for integration with motion capture (MOCAP) and electrophysiological measurements (multi-muscle EMG) systems. Throughout the project, we will also implement the methodologies for analysing these additional data. BENCH also aims at providing an initial dataset recorded from human subjects, including raw-data and several metrics such as neuro-mechanical indicators derived from multimodal measurements and clinical measurements. The database will comprise data from healthy individuals during unconstrained as well as constrained movement, the latter simulating the interaction with wearable exoskeletons. BENCH has the potential to be transformed into a clinical and biomechanical evaluation product and to be marketed to potential stakeholders (such as hospitals, research laboratories and robot manufacturing companies).