Rodrigo Ramon - Academia.edu (original) (raw)

Papers by Rodrigo Ramon

2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR)

A US Department of Energy (DOE) sponsored study conducted by the Los Alamos National Laboratory r... more A US Department of Energy (DOE) sponsored study conducted by the Los Alamos National Laboratory reported the incidence rate of repetitive stress injuries increased from 22% after 1–2 hours of work to 50% after 3 hours of work per day due to fatigue, repetitive motion, and hyperextension during manipulation tasks in glovebox and hotcell workspaces at DOE facilities [1, 2]. Because of the repetitive nature of the tasks in these facilities, a proposed exoskeleton assistive device is being developed to help reduce worker fatigue and therefore, reduce fatigue and stress related injuries. Current stages of this project focus on the design and development of an upper limb assistance exoskeleton device which aims to reduce worker fatigue through muscular involvement minimization. The design includes a low-profile frame which houses various radially guided joints which parallel human arm movement directions to allow for an ergonomic natural movement. Active assistance is seen in a vertical manner to help reduce muscleA fatigue stemming from carrying materials, as well as maintaining the weight of the worker's arm and whichever tools they may be carrying to complete the task. This work outlines the development of the mechanical and software-based control aspects of the assistive device, as well as an overall view of the many limitations which must be taken into consideration during the design and development.

Sensors, 2020

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive worklo... more Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators’ cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explo...

2017 IEEE International Conference on Mechatronics and Automation (ICMA), 2017

Within the scope of wearable robotics, human to machine interfacing is a critical element of the ... more Within the scope of wearable robotics, human to machine interfacing is a critical element of the development of an accurate, reliable, and apt device. Wearable robotics span through a varied field of rehabilitation robotics, exoskeletons designed for industrial, military and personal use, and recently, even for handicap offset. Within any of these varied uses, a key design step is defining a viable, consistent source of user intent. Without a clear vision of what the user wishes to accomplish, the robotic aid cannot behave in a way that is of any benefit to the user in question. The use of electromyography (EMG) is widely regarded as a viable means of correlating signals obtained from the body to physiological locomotion. The non-invasive nature of surface EMG serves as a comfortable method of extracting accurate musculoskeletal functions for this purpose. We purpose to accurately determine user intent through the processing and feature extraction of biological signal sources. The current work will focus on the development and methodology used to correlate joint angles with EMG signals for a planar model used in a developing neurological rehabilitation robot based on a Denso HS-4555 Selective Compliance Articulated Robot Arm (SCARA).

Journal of NeuroEngineering and Rehabilitation, 2020

Background Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is... more Background Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. Methods An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then use...

2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR)

A US Department of Energy (DOE) sponsored study conducted by the Los Alamos National Laboratory r... more A US Department of Energy (DOE) sponsored study conducted by the Los Alamos National Laboratory reported the incidence rate of repetitive stress injuries increased from 22% after 1–2 hours of work to 50% after 3 hours of work per day due to fatigue, repetitive motion, and hyperextension during manipulation tasks in glovebox and hotcell workspaces at DOE facilities [1, 2]. Because of the repetitive nature of the tasks in these facilities, a proposed exoskeleton assistive device is being developed to help reduce worker fatigue and therefore, reduce fatigue and stress related injuries. Current stages of this project focus on the design and development of an upper limb assistance exoskeleton device which aims to reduce worker fatigue through muscular involvement minimization. The design includes a low-profile frame which houses various radially guided joints which parallel human arm movement directions to allow for an ergonomic natural movement. Active assistance is seen in a vertical manner to help reduce muscleA fatigue stemming from carrying materials, as well as maintaining the weight of the worker's arm and whichever tools they may be carrying to complete the task. This work outlines the development of the mechanical and software-based control aspects of the assistive device, as well as an overall view of the many limitations which must be taken into consideration during the design and development.

Sensors, 2020

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive worklo... more Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators’ cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explo...

2017 IEEE International Conference on Mechatronics and Automation (ICMA), 2017

Within the scope of wearable robotics, human to machine interfacing is a critical element of the ... more Within the scope of wearable robotics, human to machine interfacing is a critical element of the development of an accurate, reliable, and apt device. Wearable robotics span through a varied field of rehabilitation robotics, exoskeletons designed for industrial, military and personal use, and recently, even for handicap offset. Within any of these varied uses, a key design step is defining a viable, consistent source of user intent. Without a clear vision of what the user wishes to accomplish, the robotic aid cannot behave in a way that is of any benefit to the user in question. The use of electromyography (EMG) is widely regarded as a viable means of correlating signals obtained from the body to physiological locomotion. The non-invasive nature of surface EMG serves as a comfortable method of extracting accurate musculoskeletal functions for this purpose. We purpose to accurately determine user intent through the processing and feature extraction of biological signal sources. The current work will focus on the development and methodology used to correlate joint angles with EMG signals for a planar model used in a developing neurological rehabilitation robot based on a Denso HS-4555 Selective Compliance Articulated Robot Arm (SCARA).

Journal of NeuroEngineering and Rehabilitation, 2020

Background Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is... more Background Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. Methods An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then use...