Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis.

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

Compensation for Magnetic Disturbances in Motion Estimation to Provide Feedback to Wearable Robotic Systems.

The direction of the Earth's magnetic field is used as a reference vector to determine the heading in orientation estimation with wearable sensors. However, the magnetic field strength is weak and can be easily disturbed in the vicinity of ferromagnetic materials, which may result in inaccurate estimate of orientation. This paper presents a novel method for estimating and compensating for magnetic disturbances. The compensation algorithm is implemented within a kinematic-based extended Kalman filter and is based on an assessment of the magnetic disturbance and the change of orientation in each time step. The proposed algorithm was experimentally validated by measuring the orientation of a simple mechanical system with three degrees of freedom in an artificially disturbed magnetic field. The results of the experimental evaluation show that an Kalman filter algorithm that incorporates compensating for magnetic disturbances is capable of estimating the orientation with moderate error (the absolute median errors , ) when the Earth's magnetic field is disturbed by magnetic disturbance with a magnitude equal to twice the magnitude of the Earth's own magnetic field in different directions.

Whole Body Awareness for Controlling a Robotic Transfemoral Prosthesis.

Restoring locomotion functionality of transfemoral amputees is essential for early rehabilitation treatment and for preserving mobility and independence in daily life. Research in wearable robotics fostered the development of innovative active mechatronic lower-limb prostheses designed with the goal to reduce the cognitive and physical effort of lower-limb amputees in rehabilitation and daily life activities. To ensure benefits to the users, active mechatronic prostheses are expected to be aware of the user intention and properly interact in a closed human-in-the-loop paradigm. In the state of the art various cognitive interfaces have been proposed to online decode the user's intention. Electromyography in combination with mechanical sensing such as inertial or pressure sensors is a widely adopted solution for driving active mechatronic prostheses. In this framework, researchers also explored targeted muscles re-innervation for an objective-oriented surgical amputation promoting wider usability of active prostheses. However, information kept by the neural component of the cognitive interface deteriorates in a prolonged use scenario due to electrodes-related issues, thereby undermining the correct functionality of the active prosthesis. The objective of this work is to present a novel controller for an active transfemoral prosthesis based on whole body awareness relying on a wireless distributed non-invasive sensory apparatus acting as cognitive interface. A finite-state machine controller based on signals monitored from the wearable interface performs subject-independent intention detection of functional tasks such as ground level walking, stair ascent, and sit-to-stand maneuvres and their main sub-phases. Experimental activities carried out with four transfemoral amputees (among them one dysvascular) demonstrated high reliability of the controller capable of providing 100% accuracy rate in treadmill walking even for weak subjects and low walking speeds. The minimum success rate was of 94.8% in performing sit-to-stand tasks. All the participants showed high confidence in using the transfemoral active prosthesis even without training period thanks to intuitiveness of the whole body awareness controller.

Identification of Mobile Phones Using the Built-In Magnetometers Stimulated by Motion Patterns.

We investigate the identification of mobile phones through their built-in magnetometers. These electronic components have started to be widely deployed in mass market phones in recent years, and they can be exploited to uniquely identify mobile phones due their physical differences, which appear in the digital output generated by them. This is similar to approaches reported in the literature for other components of the mobile phone, including the digital camera, the microphones or their RF transmission components. In this paper, the identification is performed through an inexpensive device made up of a platform that rotates the mobile phone under test and a fixed magnet positioned on the edge of the rotating platform. When the mobile phone passes in front of the fixed magnet, the built-in magnetometer is stimulated, and its digital output is recorded and analyzed. For each mobile phone, the experiment is repeated over six different days to ensure consistency in the results. A total of 10 phones of different brands and models or of the same model were used in our experiment. The digital output from the magnetometers is synchronized and correlated, and statistical features are extracted to generate a fingerprint of the built-in magnetometer and, consequently, of the mobile phone. A SVM machine learning algorithm is used to classify the mobile phones on the basis of the extracted statistical features. Our results show that inter-model classification (i.e., different models and brands classification) is possible with great accuracy, but intra-model (i.e., phones with different serial numbers and same model) classification is more challenging, the resulting accuracy being just slightly above random choice.

Experimental Identification of Smartphones Using Fingerprints of Built-In Micro-Electro Mechanical Systems (MEMS).

The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable.

Online phase detection using wearable sensors for walking with a robotic prosthesis.

This paper presents a gait phase detection algorithm for providing feedback in walking with a robotic prosthesis. The algorithm utilizes the output signals of a wearable wireless sensory system incorporating sensorized shoe insoles and inertial measurement units attached to body segments. The principle of detecting transitions between gait phases is based on heuristic threshold rules, dividing a steady-state walking stride into four phases. For the evaluation of the algorithm, experiments with three amputees, walking with the robotic prosthesis and wearable sensors, were performed. Results show a high rate of successful detection for all four phases (the average success rate across all subjects >90%). A comparison of the proposed method to an off-line trained algorithm using hidden Markov models reveals a similar performance achieved without the need for learning dataset acquisition and previous model training.

Differences between elite, junior and non-rowers in kinematic and kinetic parameters during ergometer rowing.

This paper presents an analysis of the rowing parameters of differently skilled rowers. The study focuses on technique dependency on stroke rate. Five elite, five junior and five non-rowers participated, and the biomechanics of rowing on an ergometer was analyzed at stroke rates of 20, 26 and 34 str/min. The results show that elite rowers use a similar, consistent rowing technique at all stroke rates, the technique of junior rowers follows similar principles, while the technique of non-rowers varies. Elite rowers' stroke length, handle motion and body posture do not change with stroke rate while the ratio of stroke phases, maximum forces, stroke work and joint loadings are constant at the same stroke rate but dependent on stroke rate. Junior rowers with stroke rate change also the stroke length. In non-rowers the differences can be observed in the handle motion and body posture during the stroke, their stroke length changes with stroke rate while the ratio of stroke phases stays constant. Although different movement execution is evident and variable with stroke rate, non-rowers demonstrate a consistent pattern at the same stroke rate. On the basis of the results, the crucial parameters that differentiate elite, junior, and non-rowers are identified.

Asymmetry in sit-to-stand movement in patients following transtibial amputation and healthy individuals.

The aim of this study was to analyse the asymmetry of sit-to-stand (STS) movement in a group of subjects following unilateral transtibial amputation (STTA) and a group of healthy subjects (HSs). Experimental measurements investigated standing-up pattern from two seat heights and at three different speeds. Body motion was measured using an optical measuring system with active markers. Floor and seat reaction forces and moments were measured by two force plates and an integrated force-moment sensor. Analysis of ankle, knee, hip and trunk inclination angles shows that STTA perform STS movement with different initial foot placement than HS, resulting in different lower extremity loadings and larger trunk inclination. Asymmetry was defined as the difference between left and right extremity parameters averaged throughout STS movement. A root-mean-square error was used to assess the asymmetry in ground reaction forces and in ankle, knee and hip angles and moments. The influence of different seat heights and velocities on asymmetry was tested using one-way ANOVA. The asymmetry of STTA and HS was affected neither in kinematic nor in kinetic parameters. Performing STS at higher speeds was found to result in decreased trunk flexion. The asymmetry assessment, as determined in this study, can be used in rehabilitation for improving STS strategies or as an evaluation tool for estimating the progress of the rehabilitation process.

Simulation and experimental study on trajectory tracking of the assistive standing-up robot / 医用生物力学

Objective To establish a new trajectory tracking algorithm combined with trapezoidal velocity, so as to realize the trajectory control of the assistive standing-up robot and help subjects complete the standing-up training. Methods Forces of the assistive standing-up robot acting on subjects were analyzed by deducing the force and moment balance equations. According to the interpolation points of the target curve, trapezoidal velocity and current position points of the end-effector, the trajectory tracking algorithm of the assistive standing-up robot was developed, and a simulation platform was built up by Simulink/Stateflow software. Based on the established Xpc target and host computer, assistive standing-up robot and 3D motion analysis system, trajectory tracking of the straight line, curves in different shapes, standing-up curve of the subjects were tested. Parameters that affected the velocity and accuracy of trajectory tracking as well as the differences in trapezoidal velocity and standing-up velocity were discovered. Results Accurate positon control of the assistive standing-up robot was achieved by trajectory tracking algorithm. The standing-up trajectory curve and trapezoidal velocity could meet the requirement of standing-up velocity for the subjects and fulfill their requirements for different curve shapes and velocities. Conclusions The assistive standing-up robot using trajectory tracking algorithm combined with trapezoidal velocity can accurately track the target curves without limitation of curve shapes, and help the standing-up training for subjects. The established simulation and test platform in consideration of different subjects’ standing-up trajectory curve, velocity and accelaraion will assist standing-up more effectively.

Comparison of four evaluation approaches in transcutaneous electrical nerve stimulation treatment in two incomplete tetraplegic subjects.

INTRODUCTION: In the present investigation, we applied the whole-hand transcutaneous electrical nerve stimulation (TENS) therapy to two incomplete tetraplegic subjects and assessed their progress with four evaluation methods. METHODS: Two spinal cord injured subjects with spastic upper extremities participated in the study. The TENS therapy was added to their regular treatment. The TENS was delivered to the subject's hands by a conductive glove. The therapy consisted of 20-min sessions each working day during a period of four weeks. The used assessment methods were: maximal force test, force tracking task, Jebsen-Taylor hand function test, and modified Ashworth scale. RESULTS: The results show increased finger muscle strength, improved motor control and hand function in both patients. The reduction of muscle tone, as assessed by the modified Ashworth scale, was observed in one subject. DISCUSSION: There was no correlation found between the Jebsen-Taylor test and the maximal force test or force tracking task in this investigation. Assessment methods are complementary to each other as each one adds new or more detailed information about level of impairment. CONCLUSION: From the comparison of four evaluation methods, it is evident that different assessments and measurements should be used in order to get better picture of patient's upper extremity impairment.

Design and evaluation of a functional electrical stimulation system for hand sensorimotor augmentation.

The aim of this study was to perform a preliminary evaluation of a new method for therapeutic exercise of grasping in patients with upper limb disability. The new method combines active voluntary exercise augmented with electrical stimulation and controlled by using force feedback. The feedback has two functions: automatic control of the intensity of electrical stimulation by minimizing the tracking error, and biofeedback to the patient on the computer screen. The force feedback is realized by the use of a newly designed adjustable hand force measuring device, which comprises two force sensors. The therapy requires from patients to volitionally try to open and close the hand while tracking the target on the screen. The system was evaluated in a pilot study in five healthy and two chronic incomplete tetraplegic subjects. Results in healthy subjects were used for reference and for stimulation controller evaluation. The therapy in incomplete tetraplegic subjects of 45-min daily session delivered during four weeks. The results of pilot study show that augmentation of voluntary grip force control with presented system is possible.

An inertial and magnetic sensor based technique for joint angle measurement.

This paper describes the design and evaluation of a miniature kinematic sensor based three dimensional (3D) joint angle measurement technique. The technique uses a combination of rate gyroscope, accelerometer and magnetometer sensor signals. The technique enables 3D inter-segment joint angle measurement and could be of benefit in a variety of applications which require monitoring of joint angles. The technique is not dependent on a fixed reference coordinate system and thus may be suitable for use in a dynamic system such as a moving vehicle. The technique was evaluated by applying it to joint angle measurement of the ankle joint. Experimental results show that accurate measurement of ankle joint angles is achieved by the technique during a variety of lower leg exercises including walking.

Human voluntary activity integration in the control of a standing-up rehabilitation robot: a simulation study.

The paper presents a novel control approach for the robot-assisted motion augmentation of disabled subjects during the standing-up manoeuvre. The main goal of the proposal is to integrate the voluntary activity of a person in the control scheme of the rehabilitation robot. The algorithm determines the supportive force to be tracked by a robot force controller. The basic idea behind the calculation of supportive force is to quantify the deficit in the dynamic equilibrium of the trunk. The proposed algorithm was implemented as a Kalman filter procedure and evaluated in a simulation environment. The simulation results proved the adequate and robust performance of "patient-driven" robot-assisted standing-up training. In addition, the possibility of varying the training conditions with different degrees of the subject's initiative is demonstrated.

Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors.

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented--Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)--and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16+/-1.8 mm in horizontal and 39+/-3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage.

Static analysis of nippers pinch.

The purpose of the study was to present a method for the assessment of finger joint torques in two-fingered precision grips. The static analysis of various grips is important for the analysis of the mechanics of a human hand and the functional evaluation of grasping. We have built a grip-measuring device assessing the endpoint forces of two-oppositional grips. Through the simultaneous use of an optical measuring system and the grip-measuring device, the finger positions and the grip force acting on the object were obtained. A recursive computational method was used within the proposed static model of the finger to calculate the finger joint torques. In the paper a three-dimensional static model of the grip is presented and the calculated finger joint torques are shown. The repeatability within subject is analyzed for the assessed grip force and finger joint torques. The estimated joint torques corresponds to the amount of load on the finger joints during the isometric muscle contraction in nippers pinch.