Lower-Limbs Exoskeletons Benchmark Exploiting a Stairs-Based Testbed: The STEPbySTEP Project

Maugliani, Nicole; Caimmi, Marco; Malosio, Matteo; Airoldi, Francesco; Borro, Diego; Rosquete, Daniel; Sergio, Ausejo; Giusino, Davide; Fraboni, Federico; Ranieri, G.; Pietrantoni, Luca; Roveda, Loris

doi:10.1007/978-3-030-69547-7_97

Cited by 4 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it is shown in Fig. 2 , the biomechanics of stair ascent can be divided into the following five distinct phases: WA, PU, CN, FC, and FP 12 , 51 – 55 . Each phase serves a specific function for successful and continuous negotiation of stairs.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…The stance phase is further divided into WA, PU, and CN phases by contralateral toe-off and foot-strike, respectively. And the swing phase is equally divided between the FC and FP phase 55 . The ensemble data (Fig.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…We hypothesize that if Myosuit does not induce large deviations that alter the individual’s lower limb kinematics, plantar force, and EMG patterns while lowering hip and/or knee extensor EMG levels, Myosuit might be a viable solution for stair ascent training. In order to evaluate the data according to phases involved with stair ascent, we dissected the gait cycle into five non-overlapping phases: weight acceptance (WA), pull up (PU), forward continuance (CN), foot clearance (FC), foot placement (FP) 12 , 51 – 55 (Fig. 2 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigation with able-bodied subjects suggests Myosuit may potentially serve as a stair ascent training robot

Kim,

Kang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Real world settings are seldomly just composed of level surfaces and stairs are frequently encountered in daily life. Unfortunately, ~ 90% of the elderly population use some sort of compensation pattern in order to negotiate stairs. Because the biomechanics required to successfully ascend stairs is significantly different from level walking, an independent training protocol is warranted. Here, we present as a preliminary investigation with 11 able-bodied subjects, prior to clinical trials, whether Myosuit could potentially serve as a stair ascent training robot. Myosuit is a soft wearable exosuit that was designed to assist the user via hip and knee extension during the early stance phase. We hypothesized that clinical studies could be carried out if the lower limb kinematics, sensory feedback via plantar force, and electromyography (EMG) patterns do not deviate from the user’s physiological stair ascent patterns while reducing hip and knee extensor demand. Our results suggest that Myosuit conserves the user’s physiological kinematic and plantar force patterns. Moreover, we observe approximately 20% and 30% decrease in gluteus maximus and vastus medialis EMG levels in the pull up phase, respectively. Collectively, Myosuit reduces the hip and knee extensor demand during stair ascent without any introduction of significant compensation patterns.

show abstract

Section: Experimental Protocolmentioning

confidence: 99%

Section: Experimental Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigation with able-bodied subjects suggests Myosuit may potentially serve as a stair ascent training robot

Kim,

Kang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This specific use scenario was chosen due to the ubiquitous presence of stairs in real-world environments, as well as the difficulty of mobility-challenged individuals in stair climbing. The assistance provided by wearable robots constitutes a promising solution to overcome such difficulty [11], and the effectiveness of wearable robot assistance can be quantified with instrumented testbeds (e.g., [12]). The proposed vision-based intent recognition system consists of two primary modules, including a staircase detection with the You Only Look Once (YOLO) v5 model [13] and a bounding-box-based intent classification algorithm constructed with the AdaBoost and gradient boost (GB) methods.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

Islam

Haque

Imtiaz

et al. 2023

Sensors

View full text Add to dashboard Cite

Walking in real-world environments involves constant decision-making, e.g., when approaching a staircase, an individual decides whether to engage (climbing the stairs) or avoid. For the control of assistive robots (e.g., robotic lower-limb prostheses), recognizing such motion intent is an important but challenging task, primarily due to the lack of available information. This paper presents a novel vision-based method to recognize an individual’s motion intent when approaching a staircase before the potential transition of motion mode (walking to stair climbing) occurs. Leveraging the egocentric images from a head-mounted camera, the authors trained a YOLOv5 object detection model to detect staircases. Subsequently, an AdaBoost and gradient boost (GB) classifier was developed to recognize the individual’s intention of engaging or avoiding the upcoming stairway. This novel method has been demonstrated to provide reliable (97.69%) recognition at least 2 steps before the potential mode transition, which is expected to provide ample time for the controller mode transition in an assistive robot in real-world use.

show abstract

“…While for lower limb functions, some ongoing researches have already adopted or proposed benchmarking methods [23][24][25][26], in the upper limb field, the benchmarking approach is still missing [3,22,27,28]. Longatelli et al Journal of NeuroEngineering and Rehabilitation (2022) 19:102 This work aims to develop the first benchmarking framework for evaluating upper limb capabilities in clinical and research settings.…”

Section: Introductionmentioning

confidence: 99%

A unified scheme for the benchmarking of upper limb functions in neurological disorders

Longatelli

Torricelli

Tornero³

et al. 2022

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary. Methods This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols. Results We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles. Conclusions This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.

show abstract

Lower-Limbs Exoskeletons Benchmark Exploiting a Stairs-Based Testbed: The STEPbySTEP Project

Cited by 4 publications

References 7 publications

Investigation with able-bodied subjects suggests Myosuit may potentially serve as a stair ascent training robot

Investigation with able-bodied subjects suggests Myosuit may potentially serve as a stair ascent training robot

Vision-Based Recognition of Human Motion Intent during Staircase Approaching

A unified scheme for the benchmarking of upper limb functions in neurological disorders

Contact Info

Product

Resources

About