Multi-Axis Force Sensor for Human–Robot Interaction Sensing in a Rehabilitation Robotic Device

Lifting and carrying heavy objects is a major aspect of physically intensive jobs. Wearable sensors have previously been used to classify different ways of picking up an object, but have seen only limited use for automatic classification of load position and weight while a person is walking and carrying an object. In this proof-of-concept study, we thus used wearable inertial and electromyographic sensors for offline classification of different load positions (frontal vs. unilateral vs. bilateral side loads) and weights during gait. Ten participants performed 19 different carrying trials each while wearing the sensors, and data from these trials were used to train and evaluate classification algorithms based on supervised machine learning. The algorithms differentiated between frontal and other loads (side/none) with an accuracy of 100%, between frontal vs. unilateral side load vs. bilateral side load with an accuracy of 96.1%, and between different load asymmetry levels with accuracies of 75–79%. While the study is limited by a lack of electromyographic sensors on the arms and a limited number of load positions/weights, it shows that wearable sensors can differentiate between different load positions and weights during gait with high accuracy. In the future, such approaches could be used to control assistive devices or for long-term worker monitoring in physically demanding occupations.

Section: Introductionmentioning

confidence: 99%

Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors

Goršič

Dai

Novak

2020

“…Over several years, haptic sensing in robotics has been a topic of considerable interest [1,2,3]. Haptic sensing is measured by means of a value such as that of an interaction force between an object and instrument.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video

Kim

Cho

Lim

et al. 2019

Interaction forces are traditionally predicted by a contact type haptic sensor. In this paper, we propose a novel and practical method for inferring the interaction forces between two objects based only on video data—one of the non-contact type camera sensors—without the use of common haptic sensors. In detail, we could predict the interaction force by observing the texture changes of the target object by an external force. For this purpose, our hypothesis is that a three-dimensional (3D) convolutional neural network (CNN) can be made to predict the physical interaction forces from video images. In this paper, we proposed a bottleneck-based 3D depthwise separable CNN architecture where the video is disentangled into spatial and temporal information. By applying the basic depthwise convolution concept to each video frame, spatial information can be efficiently learned; for temporal information, the 3D pointwise convolution can be used to learn the linear combination among sequential frames. To validate and train the proposed model, we collected large quantities of datasets, which are video clips of the physical interactions between two objects under different conditions (illumination and angle variations) and the corresponding interaction forces measured by the haptic sensor (as the ground truth). Our experimental results confirmed our hypothesis; when compared with previous models, the proposed model was more accurate and efficient, and although its model size was 10 times smaller, the 3D convolutional neural network architecture exhibited better accuracy. The experiments demonstrate that the proposed model remains robust under different conditions and can successfully estimate the interaction force between objects.

“…Usually, this interaction feedback is measured directly using position and force sensors. However, although various research groups work in this line [ 14 ], there are still no specific sensors for this type of applications. Hence, most rehabilitation robots that can be found in the literature use industrial sensors, which are not adapted for training therapies [ 15 , 16 , 17 ] and present several drawbacks: (1) rehabilitation robots usually present complex structures, and the placement of sensors in the user/robot interaction point is a challenging task; (2) the introduction of a number of sensors in wearable robots may increase their weight, which is not desirable; (3) noise and temperature dependency of the force sensors require adequate processing of sensors signals; and finally; (4) the use of accurate sensors increases the cost of robotic rehabilitation devices, reducing its market growth [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensors for Advanced Controllers in Rehabilitation Robotics

Mancisidor

Cabanes

Portillo

et al. 2018

In order to properly control rehabilitation robotic devices, the measurement of interaction force and motion between patient and robot is an essential part. Usually, however, this is a complex task that requires the use of accurate sensors which increase the cost and the complexity of the robotic device. In this work, we address the development of virtual sensors that can be used as an alternative of actual force and motion sensors for the Universal Haptic Pantograph (UHP) rehabilitation robot for upper limbs training. These virtual sensors estimate the force and motion at the contact point where the patient interacts with the robot using the mathematical model of the robotic device and measurement through low cost position sensors. To demonstrate the performance of the proposed virtual sensors, they have been implemented in an advanced position/force controller of the UHP rehabilitation robot and experimentally evaluated. The experimental results reveal that the controller based on the virtual sensors has similar performance to the one using direct measurement (less than 0.005 m and 1.5 N difference in mean error). Hence, the developed virtual sensors to estimate interaction force and motion can be adopted to replace actual precise but normally high-priced sensors which are fundamental components for advanced control of rehabilitation robotic devices.