On the Effects of Visual Anticipation of Floor Compliance Changes on Human Gait: Towards Model-based Robot-Assisted Rehabilitation

Drolet, Michael; Yumbla, Emiliano Quinones; Hobbs, Bradley; Artemiadis, Panagiotis

doi:10.1109/icra40945.2020.9197536

Cited by 9 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) In "the approach" step, hugs can be initiated anytime with a starting point that is not predetermined. In this process, adaptive coordination is essential and requires the robot to predict the partner's hug types and upcoming actions 36 . To address this issue, we would study embodied perception based on head vision and whole-body tactile sensation.…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, the HUG taxonomy is the first well-defined taxonomy of hugs. Therefore, we compare the HUG taxonomy with the hugging behavior research (see 10,14,15,21,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] ), covering the domain of humans and robots. Most reviews of the human hugging behavior research only suggest taxonomic ideas without conducting any further classification.…”

Section: Taxonomy Completenessmentioning

confidence: 99%

See 1 more Smart Citation

A HUG taxonomy of humans with potential in human–robot hugs

Yan,

Wang,

Ren

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Humans can easily perform various types of hugs in human contact and affection experience. With the prevalence of robots in social applications, they would be expected to possess the capability of hugs as humans do. However, it is still not an easy task for robots, considering the complex force and spatial constraints of robot hugs. In this work, we propose the HUG taxonomy, which distinguishes between different hugging patterns based on human demonstrations and prior knowledge. In this taxonomy, hugs are arranged according to (1) hugging tightness, (2) hugging style, and (3) bilateral coordination, resulting in 16 different hug types. We then further study the hug type preference of humans in different scenarios and roles. Furthermore, we propose a rule-based classification system to validate the potential of this taxonomy in human–robot hugs based on a humanoid robot with an E-skin of contact sensation. The HUG taxonomy could provide human hugging behavior information in advance, facilitating the action control of humanoid robots. We believe the results of our work can benefit future studies on human–robot hugging interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Taxonomy Completenessmentioning

confidence: 99%

A HUG taxonomy of humans with potential in human–robot hugs

Yan,

Wang,

Ren

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Latest findings involving early lower-limb muscle activation are reported during transitions from rigid to compliant surfaces [21], [22], [23]. Results indicate the existence of anticipatory muscle responses for both legs and that significant activity changes are present just before and immediately after an individual encounters a compliant surface.…”

mentioning

confidence: 88%

On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals

Angelidou

Artemiadis

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

View full text Add to dashboard Cite

Walking surfaces of varying compliance are encountered frequently in everyday life, and transitions between them are usually not a challenging task for most people. The human brain, based on feedback from the environment, as well as previous experience, controls the lower limb dynamics to transition to new surfaces ensuring stability and safety. However, this is not always possible for people with lower limb impairments, especially those using wearable (orthotic) or prosthetic devices. Current control methodologies for lower limb wearables and powered ankle prostheses have successfully replicated conditions for walking on rigid surfaces. However, agility and walking stability on non-flat and compliant surfaces remain a significant challenge for individuals with gait disabilities. There is therefore the need to incorporate the human wearer in the loop and proactively adjust their control to transition to surfaces of different compliance. This work proposes a subject-specific pattern recognition (PR) and classification strategy using kinematic data and surface electromyographic (EMG) signals to recognize user intent to transition from a rigid to a compliant surface. Using a k-Nearest Neighbors (k-NN) methodology in combination with an Artificial Neural Network (ANN), our strategy can accurately predict upcoming surface stiffness transitions in real time. This would allow for a fast parameter control of the prosthesis or wearable device and for adaptation to the new terrain. Classification results after employing the proposed strategy reach a prediction accuracy of up to 87.5%, proving that predicting transitions to compliant surfaces in real time is feasible and efficient. The proposed framework can lead to increased robustness and safety of lower-limb prosthetic or wearable devices that will eventually improve the quality of life of individuals living with a lower limb impairment.

show abstract

“…In the domain of motor-rehabilitation, EMG-data can be of particular importance. It can be used to demonstrate the basic effectiveness of the system by showing that users of the application really activate targeted muscles (Park et al, 2016;Drolet et al, 2020). This is of special interest when impairments do not allow visible movement of the target-limb (Patel et al, 2015).…”

Section: Physical Therapymentioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

View full text Add to dashboard Cite

Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.

show abstract

On the Effects of Visual Anticipation of Floor Compliance Changes on Human Gait: Towards Model-based Robot-Assisted Rehabilitation

Cited by 9 publications

References 21 publications

A HUG taxonomy of humans with potential in human–robot hugs

A HUG taxonomy of humans with potential in human–robot hugs

On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Contact Info

Product

Resources

About