Joint Angle Sensors Research Articles

Design and Verification of Piano Playing Assisted Hand Exoskeleton Robot.

Finger technique is a crucial aspect of piano learning, and hand exoskeleton mechanisms effectively assist novice piano players in maintaining correct finger technique consistently. Addressing current issues with exoskeleton robots, such as the inability to provide continuous correction of finger technique and their considerable weight, a novel hand exoskeleton robot has been developed to enhance finger technique through continuous correction and reduced weight. Initial data are gathered using finger joint angle sensors to analyze movements during piano playing, focusing on the trajectory and angular velocity of key strikes. This analysis informs the design of a 6-bar double-closed-loop mechanism with an end equivalent sliding pair, using analytical methods to establish the relationship between motor extension and input rod rotation. Simulation studies assess the exoskeleton's motion space and dynamics, confirming its capability to meet structural and functional demands for accurate key striking. Prototype testing validates the exoskeleton's ability to maintain correct finger positioning and mimic natural strike speeds, thus improving playing technique while ensuring comfort and safety.

Where to mount the IMU? Validation of joint angle kinematics and sensor selection for activities of daily living

We validate the OpenSense framework for IMU-based joint angle estimation and furthermore analyze the framework's ability for sensor selection and optimal positioning during activities of daily living (ADL). Personalized musculoskeletal models were created from anthropometric data of 19 participants. Quaternion coordinates were derived from measured IMU data and served as input to the simulation framework. Six ADLs, involving upper and lower limbs were measured and a total of 26 angles analyzed. We compared the joint kinematics of IMU-based simulations with those of optical marker-based simulations for most important angles per ADL. Additionally, we analyze the influence of sensor count on estimation performance and deviations between joint angles, and derive the best sensor combinations. We report differences in functional range of motion (fRoMD) estimation performance. Results for IMU-based simulations showed MAD, RMSE, and fRoMD of 4.8°, 6.6°, 7.2° for lower limbs and for lower limbs and 9.2°, 11.4°, 13.8° for upper limbs depending on the ADL. Overall, sagittal plane movements (flexion/extension) showed lower median MAD, RMSE, and fRoMD compared to transversal and frontal plane movements (rotations, adduction/abduction). Analysis of sensor selection showed that after three sensors for the lower limbs and four sensors for the complex shoulder joint, the estimation error decreased only marginally. Global optimum (lowest RMSE) was obtained for five to eight sensors depending on the joint angle across all ADLs. The sensor combinations with the minimum count were a subset of the most frequent sensor combinations within a narrowed search space of the 5% lowest error range across all ADLs and participants. Smallest errors were on average < 2° over all joint angles. Our results showed that the open-source OpenSense framework not only serves as a valid tool for realistic representation of joint kinematics and fRoM, but also yields valid results for IMU sensor selection for a comprehensive set of ADLs involving upper and lower limbs. The results can help researchers to determine appropriate sensor positions and sensor configurations without the need for detailed biomechanical knowledge.

Recognition System of Human Fatigue State Based on Hip Gait Information in Gait Patterns

Fatigue is a common phenomenon in sports and affects sports performance. The production of fatigue during running increases the risk of sports-related injury. People with high physical demands, such as construction workers, soldiers and athletes, are often in a state of muscle fatigue, which may have an adverse effect on health and safety. It is necessary to take effective preventive measures when muscle fatigue occurs. In this paper, a wearable system for monitoring hip dynamics during human walking is proposed, and a machine learning method is used to evaluate fatigue level. The fatigue level of each subject was determined by monitoring the percentage of maximum oxygen uptake. Different percentages of oxygen uptake correspond to different exercise levels. The hip joint angle sensor used herein can sense real-time changes in the angle of the human hip joint, and the data can be used to objectively evaluate the fatigue level of the human body to reduce the risk of running-related overuse injuries. This system can be applied to a human exoskeleton device without increasing the burden on the wearer.

Motion Tracking Smart Work Suit With a Modular Joint Angle Sensor Using Screw Routing

Activity data of workers can be used to manage their safety and working processes. To date, the adoption of motion measuring systems in industrial sites has been limited, due to cost and noisy environments. In this study, a motion-tracking smart work suit is presented to monitor the movements of workers. This system comprises modular joint-angle sensors that utilize a BoASensor mechanism, a conventional work suit with added sensor sleeves, and a monitoring device. The modularized joint-angle sensor can be easily attached or detached onto or from the normal work suit, improving its usability and washability. This suit could experience relative motion and deformation between the body, degrading sensor robustness, unlike conventional motion sensing suits. The screw sensor routing method is proposed to minimize the coordination effect between the sensor and skeletal system during body movements. Various postures recorded with the suit exhibited root mean square errors lower than 7.75% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ $</tex-math></inline-formula> from the elbow range of motion (150°) compared with the inertial-measurement-unit-(IMU)-based motion tracker. It was determined that there exists statistical significance between the proposed screw-routing method and the IMU in precision, thereby demonstrating sensing robustness with various upper limb motions. Additionally, the linearity analysis revealed that screw-routing has the highest linear behavior. The proposed work suit is expected to be utilized in preventing industrial accidents at various sites, owing to its low production cost and high accessibility.

A Stick on, Film-Like, Split Angle Sensor via Magnetic Induction for Versatile Applications

Foldable structures have been widely employed in industry, consumer electronics, and origami robots. Sensing the angle of foldable structures has been challenging due to the limited high strain area and the impairment of folding movement introduced by sensor integration. Despite extensive research and remarkable progress, strain measurement-based wearable joint angle sensors have been limited in “real-world” applications due to the poor reliability and the strict requirement of sensor placement. In this work, we propose a film-like split angle sensor (FSAS) that operates through AC magnetic field coupling between a soft planar coil and a ferromagnetic or conductive target film. The FSAS has a split configuration in which no electrical or mechanical connection is needed between the sensing film (soft planar coil) and the target film (ferromagnetic or conductive material), overcoming the movement impairment problem presented by the strain sensing approach. Moreover, it inherits the advantages of existing inductive sensors, such as no hysteresis, high sensitivity, and insensitivity to local deformation. The results show that an FPC coil with a copper foil as sensing target can distinguish a 0.007° angle change when the angle between the sensing film and the target film is 30°. Both finite element modeling and experimental results indicate that a long rectangular coil or square coil with a small distance between the coil edge and the sensing target film can achieve higher sensitivity and larger effective sensing range. Finally, the FSAS was demonstrated for angle sensing and external force detection of a laptop lid, for wearable angle monitoring of elbow, and for perception of a pneumatic driven origami robot, highlighting its high performance, versatility, and extreme easy-to-implementation features.

Versatile and Suitable for Harsh Environments-Capacitive Proximity Perception in Robotics

Robotic systems are becoming increasingly present in every aspect of our daily private as well as working environments. The more these systems enter into our spheres of action, the more they are intended to support humans in dangerous, dull and dirty tasks, and the more flexibility is expected from them. In order to prepare robotic systems for such duties, they need to be able to perceive their environment. In robotic systems, a perception of the environment can either be achieved through internal sensors, i.e., joint angle and torque sensors, or through external sensors, e.g., vision or tactile sensors. While the use of such internal sensors may seem advantageous because no additional system has to be added, the information that can be obtained from them is rather undifferentiated when we want to gather information about the environment. Thus, this information is in most cases augmented by external sensors. Considering exteroception, the most often used technology to analyze the environment is vision. However, due to general deficiencies of the system such as their dependence on lighting conditions and the necessary line of sight, the information accessible through vision alone [1] is not sufficient to accomplish differentiated manipulation tasks [2].

Mapping Surgeons Hand/Finger Movements to Surgical Tool Motion During Conventional Microsurgery Using Machine Learning

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human–robot interfaces lack intuitive teleoperation and cannot mimic surgeon’s hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon’s gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon’s feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument’s pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon’s hand/finger movements with a mean squared error (MSE) less than 0.3%. Conclusion: We have developed a system to capture fine movements of the surgeon’s hand during micro-surgery that could enhance future remote teleoperation of similar surgical tools during micro-surgery. More work is needed to refine this approach and confirm its potential role in teleoperation.

Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization.

A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.

Design and Experimental Evaluation of a Semi-Passive Upper-Limb Exoskeleton for Workers With Motorized Tuning of Assistance.

This article presents the H-PULSE, a novel semi-passive upper-limb exoskeleton for worker assistance, with motorized tuning of the assistive level. The H-PULSE presents novel design features compared to other passive industrial exoskeletons for the upper limbs, namely joint angle sensors for measuring shoulder flexion/extension and a novel active mechanism for regulating the assistance level. These features could enhance the effectiveness of the system. Along with the presentation of the exoskeleton design, this article reports on the system experimental evaluation with human subjects. The H-PULSE was assessed in prolonged static overhead tasks under different conditions of assistive support. The set of metrics to evaluate the effects of the device included shoulder muscular activity, heart rate, and subjective user feedback. Results show that the exoskeleton can reduce the users' muscular activity and the heart rate. Subjective questionnaires allowed the assessment of perceived exoskeleton effectiveness. In this study, the H-PULSE exoskeleton was found to be potentially effective in reducing the muscular strain while reducing the global fatigue level during prolonged continuous overhead activities.

Performance Analysis of a Lower Limb Multi Joint Angle Sensor Using CYTOP Fiber: Influence of Light Source Wavelength and Angular Velocity Compensation.

This paper presents the analysis of an intensity variation polymer optical fiber (POF)-based angle sensor performance, i.e., sensitivity, hysteresis and determination coefficient (), using cyclic transparent optical polymer (CYTOP) fiber. The analysis consisted of two approaches: influence of different light source central wavelengths (430 nm, 530 nm, 660 nm, 870 nm and 950 nm) and influence of different angular velocities ( rad/s, rad/s, rad/s, rad/s and rad/s). The first approach aimed to select the source which resulted in the most suitable performance regarding highest sensitivity and linearity while maintaining lowest hysteresis, through the figure of merit. Thereafter, the analysis of different angular velocities was performed to evaluate the influence of velocity in the curvature sensor performance. Then, a discrete angular velocity compensation was proposed in order to reduce the root-mean-square error (RMSE) of responses for different angular velocities. Ten tests for each analysis were performed with angular range of to , based on knee and ankle angle range during the gait. The curvature sensor was applied in patterns simulating the knee and ankle during the gait. Results show repeatability and the best sensor performance for nm in the first analysis and show high errors for high angular velocities ( rad/s) in the second analysis, which presented up to angular error. The uncompensated RMSE was high for all velocities ( to ), whereas the compensated RMSE decreased up to ( to ). The compensated responses of application tests showed maximum error of and minimum of , presenting a decrease of mean angular error up to when compared with uncompensated responses.

Mobile, Low Profile, and Inexpensive Knee Joint Angle Sensor

Mobile, Low Profile, and Inexpensive Knee Joint Angle Sensor

ウェアラブル指先力計測センサシステムの開発

In order to perform the rehabilitation of a finger effectively, it is important to measure finger force of patients without inhibiting the grip sensation and to give appropriate advice using the force data. Therefore, in previous study, we focused on the relationship between the tension of tendon and finger force, and proposed a ring type sensor that can estimate the finger force from tension of tendon. In this study, the influence of the finger postures on the ring type sensor output is investigated. First, relationship between the finger joint angle and the ring type finger force sensor output is investigated by using a wearable sensor integrated with finger joint angle sensor. From the results, a compensation method of the finger force using finger joint angle sensor output is constructed. In addition, finger force estimation experiments with using the integrated wearable sensor were conducted. In conclusion, it is confirmed that the integrated wearable sensor is useful for estimating finger force regardless of finger postures.

A Joint-Angle Estimation Method for Industrial Manipulators Using Inertial Sensors

This paper focuses on the use of low-cost inertial sensors in industrial manipulators. In particular, an easy-to-install joint-angle estimation method is proposed. This scheme can work independently from the primary encoder-based sensor, for the creation of a fault-detection system. It can also be the primary joint-angle sensor in low-cost robotic manipulators. A robot with six degrees of freedom has been used, with three inertial measurement units set on its arms; one for each pair of joints. Three cascaded extended Kalman filters have been used to estimate the joint angles by the fusion of the outputs of triaxial gyroscopes and accelerometers. The results of three experimental tests are reported in order to show the performance and limitations of this approach, in comparison with the simple gyroscope measurements integration.

Precise Haptic Device Co-Location for Visuo-Haptic Augmented Reality.

Visuo-haptic augmented reality systems enable users to see and touch digital information that is embedded in the real world. PHANToM haptic devices are often employed to provide haptic feedback. Precise co-location of computer-generated graphics and the haptic stylus is necessary to provide a realistic user experience. Previous work has focused on calibration procedures that compensate the non-linear position error caused by inaccuracies in the joint angle sensors. In this article we present a more complete procedure that additionally compensates for errors in the gimbal sensors and improves position calibration. The proposed procedure further includes software-based temporal alignment of sensor data and a method for the estimation of a reference for position calibration, resulting in increased robustness against haptic device initialization and external tracker noise. We designed our procedure to require minimal user input to maximize usability. We conducted an extensive evaluation with two different PHANToMs, two different optical trackers, and a mechanical tracker. Compared to state-of-the-art calibration procedures, our approach significantly improves the co-location of the haptic stylus. This results in higher fidelity visual and haptic augmentations, which are crucial for fine-motor tasks in areas such as medical training simulators, assembly planning tools, or rapid prototyping applications.

Fuzzy-logic-based hybrid locomotion mode classification for an active pelvis orthosis: Preliminary results.

In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%.

Finger motion capture from wrist-electrode contact resistance.

Hand motion capture is an important yet challenging topic for biomechanics and human computer interaction. We proposed a novel electrical sensing technology for capturing the finger angles from the variation of the wrist shape. The proposed device detects the signal related to the wrist-electrode contact resistances, which change according to the variation of the wrist shape accompanying finger movements. The developed sensing device consists of a wrist band, sixteen electrodes and a sensing circuit of contact resistances. We investigated the relationships between the finger angles and the system outputs by using a glove-type joint angle sensor. As a result, we confirmed high correlations of the system outputs with the finger angles for several electrodes. Therefore, we conclude that the proposed system can be used for the estimation of the finger joint angles.

관절각 측정을 위한 적외선 각도 센서 연구

Joint angle generally can be measured to check the recovery process in rehabilitation field. This paper suggests to measure joint angle with Infrared sensor based on triangulation principle. We performed various experiments to find the optimal condition of the sensor’s attached distance, height, and angle. The results were compared with commercial goniometer for accuracy. The proposed infrared joint angle sensor can be effectively used in rehabilitation and sport science fields.

1P1-W09 RGB-Dセンサを用いたロボットマニピュレータの姿勢推定法(ロボットビジョン)

Most of robots has link mechanisms consisting of several links and joints, and the joint angle sensors are used to measure the poses. If any one of joint angle sensors breaks down, the robot cannot know its correct pose and can not move correctly. And, if there is a gap between the true value and the sensor value of the joint angle, the robot can not also know its pose. So, if the joint angles and the pose can be estimated from a camera image, the robot can solve the above problems and be controlled without joint angle sensors. In this paper, we propose an estimation method of robot manipulator's poses by only a RGB-D sensor without joint angle sensors. In the proposed method, the 3D model of the robot is compared with the acquired RGB-D images, and the joint angles are estimated by using the depth information and the color information. The proposed method was verified by using a multifingered robot hand.

Multi-Channel Detection System for Joint Position Based on Optical and Orthogonal Decoding

The multi-channel joint position detection system of measuring arm is of high precision, multi-channel and other special characteristic. This system uses hardware called DSPic30F microchip as the processing core. The detection system adopts optical encoder as joint angle sensor and orthogonal decoding and reversible chip HCTL-2032 to multi frequency the detected signal, which greatly reduces the system error and enhances the detection precision. Experiments of joint position detection are realized. Thirty times repeated experiments show that the maximum error is 0.009 °and the maximum relative deviation is 40.9 × 10-3 /%.

A Multifunctional Joint Angle Sensor with Measurement Adaptability

The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF) with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.