Université de Sherbrooke

Part-time lecturer (academic teacher)

• Revise and prepare educational materials.
• Assist and support students in problem solving.

Université Laval

Ph.D. Student

Cable-Driven Locomotion Interface for gait training, serious game and haptic applications.

McGill University

Postdoctoral research fellow

Vibrotactile Biofeedback, enactive shoe, enactive floor, HCI

Université du Québec à Chicoutimi

Professor

Professor in electrical and mechanical engineering (mechatronics). Research interests in haptic and human robot-interaction for industrial and health applications.

CGI

Consulting Director, expert

Martin J.D. worked at CGI as a Consulting Director, expert

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities

IEEE International Symposium on RObotic and SEnsors Environments (ROSE)

In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment. Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities. Available from: https://www.researchgate.net/publication/268632214_Safer_Hybrid_Workspace_Using_Human-Robot_Interaction_While_Sharing_Production_Activities [accessed Jun 1, 2015].

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities

IEEE International Symposium on RObotic and SEnsors Environments (ROSE)

In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment. Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities. Available from: https://www.researchgate.net/publication/268632214_Safer_Hybrid_Workspace_Using_Human-Robot_Interaction_While_Sharing_Production_Activities [accessed Jun 1, 2015].

A time-domain vibration observer and controller for physical human-robot interaction

Elsevier Mechatronics

This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller.

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities

IEEE International Symposium on RObotic and SEnsors Environments (ROSE)

In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment. Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities. Available from: https://www.researchgate.net/publication/268632214_Safer_Hybrid_Workspace_Using_Human-Robot_Interaction_While_Sharing_Production_Activities [accessed Jun 1, 2015].

A time-domain vibration observer and controller for physical human-robot interaction

Elsevier Mechatronics

This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller.

Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI

Elsevier

Touch screens as a mean for interacting with mobile applications are limited. Since the hands are already busy handling the phone or tablet, this paper proposes an innovative solution in handling digital entities with the feet. A three-axis accelerometer is arranged on a shoe in order to recognize its movement and to determine its position. Extraction of both information improves mobile interaction in different situations, especially in gaming and working in limited space. The contribution of this paper is an algorithm designed in order to extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities

IEEE International Symposium on RObotic and SEnsors Environments (ROSE)

In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment. Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities. Available from: https://www.researchgate.net/publication/268632214_Safer_Hybrid_Workspace_Using_Human-Robot_Interaction_While_Sharing_Production_Activities [accessed Jun 1, 2015].

A time-domain vibration observer and controller for physical human-robot interaction

Elsevier Mechatronics

This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller.

Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI

Elsevier

Touch screens as a mean for interacting with mobile applications are limited. Since the hands are already busy handling the phone or tablet, this paper proposes an innovative solution in handling digital entities with the feet. A three-axis accelerometer is arranged on a shoe in order to recognize its movement and to determine its position. Extraction of both information improves mobile interaction in different situations, especially in gaming and working in limited space. The contribution of this paper is an algorithm designed in order to extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

An Efficient Home-Based Risk of Falling Assessment Test Based on Smartphone and Instrumented Insole

IEEE International Symposium on Medical Measurements and Applications

The aim of this study is to improve and facilitate the methods used to assess risk of falling among older people at home. We propose an automatic version of One-Leg Standing (OLS) test for risk of falling assessment by using a Smartphone and an instrumented insole. For better clinical assessment tests, this study focuses on exploring methods to combine the most important parameters of risk of falling into a single score. Twenty-three (23) volunteers participated in this study for evaluating the effectiveness of the proposed system which includes eleven (11) elderly participants: seven (7) healthy elderly (67.16±4.24), four (4) Parkinson disease (PD) subjects (70±12.73) and twelve (12) healthy young adults (28.27±3.74). Our work suggests that there is an inverse relationship between OLS score proposed and risk of falling. Proposed instrumented insole and application running on Android could be useful at home as a diagnostic aid tool for analyzing the performance of elderly people in OLS test.

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities

IEEE International Symposium on RObotic and SEnsors Environments (ROSE)

In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment. Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities. Available from: https://www.researchgate.net/publication/268632214_Safer_Hybrid_Workspace_Using_Human-Robot_Interaction_While_Sharing_Production_Activities [accessed Jun 1, 2015].

A time-domain vibration observer and controller for physical human-robot interaction

Elsevier Mechatronics

This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller.

Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI

Elsevier

Touch screens as a mean for interacting with mobile applications are limited. Since the hands are already busy handling the phone or tablet, this paper proposes an innovative solution in handling digital entities with the feet. A three-axis accelerometer is arranged on a shoe in order to recognize its movement and to determine its position. Extraction of both information improves mobile interaction in different situations, especially in gaming and working in limited space. The contribution of this paper is an algorithm designed in order to extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

An Efficient Home-Based Risk of Falling Assessment Test Based on Smartphone and Instrumented Insole

IEEE International Symposium on Medical Measurements and Applications

The aim of this study is to improve and facilitate the methods used to assess risk of falling among older people at home. We propose an automatic version of One-Leg Standing (OLS) test for risk of falling assessment by using a Smartphone and an instrumented insole. For better clinical assessment tests, this study focuses on exploring methods to combine the most important parameters of risk of falling into a single score. Twenty-three (23) volunteers participated in this study for evaluating the effectiveness of the proposed system which includes eleven (11) elderly participants: seven (7) healthy elderly (67.16±4.24), four (4) Parkinson disease (PD) subjects (70±12.73) and twelve (12) healthy young adults (28.27±3.74). Our work suggests that there is an inverse relationship between OLS score proposed and risk of falling. Proposed instrumented insole and application running on Android could be useful at home as a diagnostic aid tool for analyzing the performance of elderly people in OLS test.

Qualitative Risk of Falling Assessment Based on Gait Abnormalities

IEEE International Conference on Systems, Man, and Cybernetics

Walking in an unfamiliar environment may include some risks of falling. For frail seniors, these risks can be significantly increased according to their ability to maintain balance. Among several factors, the user's balance can be affected by several risks including the characteristics of the user's gait. To evaluate this issue, this paper presents three methods. The first uses a statistical model while the two others exploit an Artificial Neural Network (ANN). The latter two can be differentiated by the use of constraints applied onto the raw data. Centered on non-invasive augmented shoes, our proposed system uses mobile technology to provide an on-site assistance to users, replacing the bulky equipment usually needed for clinical gait analysis. The experimental framework is based on visual disturbances to induce variation in the parameters of the user's gait. Preliminary results obtained from this framework suggest that our models enable a risk level classification.

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities

IEEE International Symposium on RObotic and SEnsors Environments (ROSE)

In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment. Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities. Available from: https://www.researchgate.net/publication/268632214_Safer_Hybrid_Workspace_Using_Human-Robot_Interaction_While_Sharing_Production_Activities [accessed Jun 1, 2015].

A time-domain vibration observer and controller for physical human-robot interaction

Elsevier Mechatronics

This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller.

Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI

Elsevier

Touch screens as a mean for interacting with mobile applications are limited. Since the hands are already busy handling the phone or tablet, this paper proposes an innovative solution in handling digital entities with the feet. A three-axis accelerometer is arranged on a shoe in order to recognize its movement and to determine its position. Extraction of both information improves mobile interaction in different situations, especially in gaming and working in limited space. The contribution of this paper is an algorithm designed in order to extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

An Efficient Home-Based Risk of Falling Assessment Test Based on Smartphone and Instrumented Insole

IEEE International Symposium on Medical Measurements and Applications

The aim of this study is to improve and facilitate the methods used to assess risk of falling among older people at home. We propose an automatic version of One-Leg Standing (OLS) test for risk of falling assessment by using a Smartphone and an instrumented insole. For better clinical assessment tests, this study focuses on exploring methods to combine the most important parameters of risk of falling into a single score. Twenty-three (23) volunteers participated in this study for evaluating the effectiveness of the proposed system which includes eleven (11) elderly participants: seven (7) healthy elderly (67.16±4.24), four (4) Parkinson disease (PD) subjects (70±12.73) and twelve (12) healthy young adults (28.27±3.74). Our work suggests that there is an inverse relationship between OLS score proposed and risk of falling. Proposed instrumented insole and application running on Android could be useful at home as a diagnostic aid tool for analyzing the performance of elderly people in OLS test.

Qualitative Risk of Falling Assessment Based on Gait Abnormalities

IEEE International Conference on Systems, Man, and Cybernetics

Walking in an unfamiliar environment may include some risks of falling. For frail seniors, these risks can be significantly increased according to their ability to maintain balance. Among several factors, the user's balance can be affected by several risks including the characteristics of the user's gait. To evaluate this issue, this paper presents three methods. The first uses a statistical model while the two others exploit an Artificial Neural Network (ANN). The latter two can be differentiated by the use of constraints applied onto the raw data. Centered on non-invasive augmented shoes, our proposed system uses mobile technology to provide an on-site assistance to users, replacing the bulky equipment usually needed for clinical gait analysis. The experimental framework is based on visual disturbances to induce variation in the parameters of the user's gait. Preliminary results obtained from this framework suggest that our models enable a risk level classification.

Modeling of physical human–robot interaction: Admittance controllers applied to intelligent assist devices with large payload

SAGE International Journal of Advanced Robotic Systems

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload.

Cartesian Control of a Cable-Driven Haptic Mechanism

Advances in Haptics

A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection

IEEE Robotic and Sensors Environments (ROSE)

It is known that head gesture and brain activity can reflect some human behaviors related to a risk of accident when using machine-tools. The research presented in this paper aims at reducing the risk of injury and thus increase worker safety. Instead of using camera, this paper presents a Smart Safety Helmet (SSH) in order to track the head gestures and the brain activity of the worker to recognize anomalous behavior. Information extracted from SSH is used for computing risk of an accident (a safety level) for preventing and reducing injuries or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of an Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reaches a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process. A Smart Safety Helmet using IMU and EEG sensors for worker fatigue detection. Available from: https://www.researchgate.net/publication/268504145_A_Smart_Safety_Helmet_using_IMU_and_EEG_sensors_for_worker_fatigue_detection [accessed Jun 1, 2015].

Geometric Determination of the Interference-Free Constant-Orientation Workspace of Parallel Cable-Driven Mechanisms

Journal of Mechanisms and Robotics

Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities

IEEE International Symposium on RObotic and SEnsors Environments (ROSE)

In a near future, human and industrial manipulator will work together sharing a common workspace and production activities leading to a potential increase of accident. The research project concerns the adaptation of industrial robot already installed in a flexible manufacturing system in order to make it more interactive with human. The aim concerns the reduction of potential risk of injuries while working with an industrial robot. This paper presents a new inexpensive, non-intrusive, non-invasive, and non-vision-based system, for human detection and collision avoidance. One method investigated for improving safety concerns planning of safe path. This system recognizes human activities and locates operator's position in real time through an instrumented safety helmet. This safety helmet includes an IMU (Inertial Measurement Unit) and an indoor localization system such as RSSI (Received Signal Strength Indication) using industrial wireless equipment. A hybrid workspace including a flexible manufacturing system has been designed in order to practice experiments in an industrial-like environment. Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities. Available from: https://www.researchgate.net/publication/268632214_Safer_Hybrid_Workspace_Using_Human-Robot_Interaction_While_Sharing_Production_Activities [accessed Jun 1, 2015].

A time-domain vibration observer and controller for physical human-robot interaction

Elsevier Mechatronics

This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller.

Use of a 3DOF Accelerometer for Foot Tracking and Gesture Recognition in Mobile HCI

Elsevier

Touch screens as a mean for interacting with mobile applications are limited. Since the hands are already busy handling the phone or tablet, this paper proposes an innovative solution in handling digital entities with the feet. A three-axis accelerometer is arranged on a shoe in order to recognize its movement and to determine its position. Extraction of both information improves mobile interaction in different situations, especially in gaming and working in limited space. The contribution of this paper is an algorithm designed in order to extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

An Efficient Home-Based Risk of Falling Assessment Test Based on Smartphone and Instrumented Insole

IEEE International Symposium on Medical Measurements and Applications

The aim of this study is to improve and facilitate the methods used to assess risk of falling among older people at home. We propose an automatic version of One-Leg Standing (OLS) test for risk of falling assessment by using a Smartphone and an instrumented insole. For better clinical assessment tests, this study focuses on exploring methods to combine the most important parameters of risk of falling into a single score. Twenty-three (23) volunteers participated in this study for evaluating the effectiveness of the proposed system which includes eleven (11) elderly participants: seven (7) healthy elderly (67.16±4.24), four (4) Parkinson disease (PD) subjects (70±12.73) and twelve (12) healthy young adults (28.27±3.74). Our work suggests that there is an inverse relationship between OLS score proposed and risk of falling. Proposed instrumented insole and application running on Android could be useful at home as a diagnostic aid tool for analyzing the performance of elderly people in OLS test.

Qualitative Risk of Falling Assessment Based on Gait Abnormalities

IEEE International Conference on Systems, Man, and Cybernetics

Walking in an unfamiliar environment may include some risks of falling. For frail seniors, these risks can be significantly increased according to their ability to maintain balance. Among several factors, the user's balance can be affected by several risks including the characteristics of the user's gait. To evaluate this issue, this paper presents three methods. The first uses a statistical model while the two others exploit an Artificial Neural Network (ANN). The latter two can be differentiated by the use of constraints applied onto the raw data. Centered on non-invasive augmented shoes, our proposed system uses mobile technology to provide an on-site assistance to users, replacing the bulky equipment usually needed for clinical gait analysis. The experimental framework is based on visual disturbances to induce variation in the parameters of the user's gait. Preliminary results obtained from this framework suggest that our models enable a risk level classification.

Modeling of physical human–robot interaction: Admittance controllers applied to intelligent assist devices with large payload

SAGE International Journal of Advanced Robotic Systems

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload.

Measuring Operator’s Pain: Toward Evaluating Musculoskeletal Disorder at Work

IEEE International Conference on Systems, Man, and Cybernetics

Musculoskeletal disorders (MSDs) have affected an increasing number of people in the active general population. In this perspective, we developed a measuring tool taking muscle activities in certain regions of the body, standing posture taking the center of pressure under the feet and feet positions. This tool also comprises an instrumented helmet containing an electroencephalogram (EEG) to measure brain activity, and an accelerometer reporting the movements of the head. Then, our tool comprises both non-invasive instrumented insole and safety helmet. Moreover, the same tool measures muscular activities in specific regions of the body using an electromyogram (EMG). The aim is to combine all the data in order to identify consistent patterns between brain activity, postures, movements and muscle activity, and then, understand their connection to the development of MSDs. This paper presents three situations reported to be a risk for MSDs and an analysis of the signals is presented in order to differentiate adequate or abnormal posture.