Kinematic Sensors Research Articles

Estimation of Forces Acting on a Sailboat Using a Kinematic Sensor

Amateur keel boat racing has becoming more and more popular on coastal regions with an old sailing tradition, such as in Adriatic. Although the traditional experience is transmitted to new generations of skippers and crews that compete on national and international regattas with open class boat, the material limits of the boat are often measured subjectively, by sail or mast failure, and transmitted by a story. Most sailors know why some component failed, but often they don’t know what force was needed for the particular failure. Forces acting on a boat are usually estimated with CFD and towing tank experiment for relevant sailing conditions, but full scale data in seaway are rarely taken. Here we wanted to show a low budget method to get a rough estimate of aerodynamic and hydrodynamic forces acting on a keel sailboat using a kinematic sensor. Some approximations are taken into account to construct a simplified mathematical sailboat model, which allow to relate kinematic data to forces acting on sail, hull, keel and rudder. Some data as the geometry and mass distribution of the boat has to be known, other parameters such as water resistance instead has to be experimentally measured. Results of a series of measurements are presented and discussed. Looking on the limitations of such a method, a proposal for a new sensor is made.

Contact Force and Joint Torque Estimation Using Skin

In this letter, we present algorithms to estimate contact locations, external forces, and joint torques using skin, i.e., distributed tactile sensors, kinematic sensors, and a single Inertial Measurement Unit (IMU) without the need for force-torque sensors. Distributed tactile sensors are an array of discrete tactile sensors (taxels) with gaps between them. To cope with the gaps, we use interpolation techniques to improve accuracy of the measurements in the skin. Then, we estimate the external force. Finally, we use this information combined with the contact location obtained from the skin to estimate the joint torques. Validation was performed using the iCub humanoid robot. Results show a performance comparable to the joint torque estimation using expensive force-torque sensors. Since the skin is less expensive and does not require modification of the internal structure of the robot, using the skin is an interesting solution.

Predicting Walking Intentions using sEMG and Mechanical sensors for various environment.

Predicting the motion intentions of a user is very challenging when controlling an exoskeleton robot. When only a mechanical sensor is used, a change in the motion is detected during the user's movement. An electromyographic (EMG) signal, which is a biological signal, is detected by the activation of the muscles before the actual movement of a person. Using the EMG signal, the motion intention can be identified before the actual movement, and the delay in time in controlling the exoskeletal robot can be shortened to reduce the resistance felt by the user. In this paper, the surface electromyographic (sEMG) signal is used together with a mechanical sensor to identify the walking environment according to the walking gait cycle. In the classification, the combination of sensors was varied, and information from one leg and two legs was analyzed by the different gait periods before and after heel contact and toe off. As a result of the classification into three sensor combinations, sEMG, kinetic, and kinematic sensors, at the pre heel contact time before walking, a 96.8% and 98.6% accuracy was obtained for information from one and two legs, respectively. In the same gait environment, it was shown that the gait prediction can be performed based on the time unit by dividing the time interval before starting the gait. An average accuracy of 84.4% was obtained when the time was divided by the environment in intervals of 100ms before heel contact, and the average was 90.9% when it was divided by an interval of 200ms before heel contact.

Comparison of video-based and sensor-based head impact exposure.

Previous research has sought to quantify head impact exposure using wearable kinematic sensors. However, many sensors suffer from poor accuracy in estimating impact kinematics and count, motivating the need for additional independent impact exposure quantification for comparison. Here, we equipped seven collegiate American football players with instrumented mouthguards, and video recorded practices and games to compare video-based and sensor-based exposure rates and impact location distributions. Over 50 player-hours, we identified 271 helmet contact periods in video, while the instrumented mouthguard sensor recorded 2,032 discrete head impacts. Matching video and mouthguard real-time stamps yielded 193 video-identified helmet contact periods and 217 sensor-recorded impacts. To compare impact locations, we binned matched impacts into frontal, rear, side, oblique, and top locations based on video observations and sensor kinematics. While both video-based and sensor-based methods found similar location distributions, our best method utilizing integrated linear and angular position only correctly predicted 81 of 217 impacts. Finally, based on the activity timeline from video assessment, we also developed a new exposure metric unique to American football quantifying number of cross-verified sensor impacts per player-play. We found significantly higher exposure during games (0.35, 95% CI: 0.29–0.42) than practices (0.20, 95% CI: 0.17–0.23) (p<0.05). In the traditional impacts per player-hour metric, we observed higher exposure during practices (4.7) than games (3.7) due to increased player activity in practices. Thus, our exposure metric accounts for variability in on-field participation. While both video-based and sensor-based exposure datasets have limitations, they can complement one another to provide more confidence in exposure statistics.

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use.

BackgroundA new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods).ObjectiveThe goal of this study was to analyze the accuracy of this algorithm under real conditions of use.MethodsThis validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm’s outputs were compared with the patients’ records, which were used as the gold standard.ResultsThe algorithm produced 37% more results than the patients’ records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients’ records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%.ConclusionsThe kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting.

A wearable, fibroid, self-powered active kinematic sensor based on stretchable sheath-core structural triboelectric fibers

While the emerging stretchable electronic sensors have been demonstrated as promising wearable functional devices, challenges in achieving highly stretchable and self-powered fiber-like sensors still exist. Here, a stretchable sheath-core structural triboelectric fiber (SSCTEF) is developed to serve as a self-powered multifunctional kinematic sensor. Owing to the advanced built-in wavy structure design, the fiber-like sensor exhibits an ultrahigh working strain (100%) and demonstrates high sensitivity in response to not only stretching but also to bending and compressing. The working principle of the SSCTEF is verified by the coupling of numerical calculations and experimental measurements. A comprehensive study is carried out to investigate the factors that influence the output performance of the SSCTEF. By wearing, it is capable of detecting and discriminating the joint movements of human bodies. By further weaving and construction, it also shows potential for detecting the deformation in two-dimensional region. This work provides new opportunities for wearable and self-powered sensing fibers with full potential in human motion monitoring.

Learning classifier to evaluate movement quality in unassisted pick-and-place exercises for post-stroke patients: A preliminary study.

The use of service robots is gaining increasing interest in stroke rehabilitation research. In autonomous robot therapy sessions, it is important to monitor the qualitative motor performance of the patient and to evaluate if therapeutically desirable movements are reinforced as per the therapist's instructions. Research on computational means to address this issue has received little attention, despite the acknowledged importance thereof. This paper poses the question of whether the subjective evaluation of the therapist on the qualitative motor performance of the individual patient can be modelled. In an attempt to answer this question, we frame it as a supervised machine learning problem and investigate if kinematic sensor readings to the subjective evaluation of the therapist can be correlated. We further examine if the personalized classifier trained with the data of the individual patient should be favored over the general classifier. Our preliminary results suggest that, given kinematic observation, trained classifiers may be able to imitate the evaluation of the therapist for the movement quality of the post-stroke patient. In our experiments, the general classifier outperformed the personalized classifiers, however, further investigation is necessary to generalize this finding.

An engineered home environment for untethered data telemetry from nonhuman primates

BackgroundWireless neural recording technologies now provide untethered access to large populations of neurons in the nonhuman primate brain. Such technologies enable long-term, continuous interrogation of neural circuits and importantly open the door for chronic neurorehabilitation platforms. For example, by providing continuous consistent closed loop feedback from a brain machine interface, the nervous system can leverage plasticity to integrate more effectively into the system than would be possible in short experimental sessions. However, to fully realize this opportunity necessitates the development of experimental environments that do not hinder wireless data transmission. Traditional nonhuman primate metal cage construction, while durable and standardized around the world, prevents data transmission at the frequencies necessary for high-bandwidth data transfer. New methodTo overcome this limitation, we have engineered and constructed a radio-frequency transparent home environment for nonhuman primates using primarily non-conductive materials. ResultsComputational modeling and empirical testing were performed to demonstrate the behavior of transmitted signals passing through the enclosure. In addition, neural data were successfully recorded from a freely behaving nonhuman primate inside the housing system. Comparison with existing methodsOur design outperforms standard metallic home cages by allowing radiation to transmit beyond its boundaries, without significant interference, while simultaneously maintaining the mechanical and operational integrity of existing commercial home cages. ConclusionsContinuous access to neural signals in combination with other bio-potential and kinematic sensors will empower new insights into unrestrained behavior, aid the development of advanced neural prostheses, and enable neurorehabilitation strategies to be employed outside traditional environments.

Inertial and time-of-arrival ranging sensor fusion

Wearable devices with embedded kinematic sensors including triaxial accelerometers, gyroscopes, and magnetometers are becoming widely used in applications for tracking human movement in domains that include sports, motion gaming, medicine, and wellness. The kinematic sensors can be used to estimate orientation, but can only estimate changes in position over short periods of time. We developed a prototype sensor that includes ultra wideband ranging sensors and kinematic sensors to determine the feasibility of fusing the two sensor technologies to estimate both orientation and position. We used a state space model and applied the unscented Kalman filter to fuse the sensor information. Our results demonstrate that it is possible to estimate orientation and position with less error than is possible with either sensor technology alone. In our experiment we obtained a position root mean square error of 5.2cm and orientation error of 4.8° over a 15min recording.

Speech adaptation to kinematic recording sensors

Measuring articulator movements can yield valuable insights into speech motor control, but attaching sensors to the articulators can affect speech. In order to measure the process of adaptation to the presence of kinematic sensors, 20 native English speakers read a sentence before the attachment of sensors to the tongue, lips, and jaw. They read it immediately afterwards, and then 5, 10, 15, and 20 minutes later. Participants read aloud continuously between recordings to encourage adaptation. Audio recordings were rated by 20 native English listeners using a visual analog scale with the endpoints labeled as precise and imprecise. Acoustic analysis involved segmenting the fricatives /s/ and /ʃ/ from the longer recording and computing spectral center of gravity and spectral standard deviation as well as fricative duration. Results of both perceptual and acoustic analysis revealed a change in speech precision over time, with all post attachment recordings receiving reduced precision ratings. Spectral mean decreased and standard deviation increased following sensor attachment; durations decreased over time. Precision ratings beyond the ten minute recording remained steady. The results show that speakers reached a height of adaptation after 10 minutes but did not at any point return to pre attachment precision levels.

WristEye: Wrist-Wearable Devices and a System for Supporting Elderly Computer Learners

Today, there is an increase in the number of elderly people who want to start using computers and the Internet. However, elderly people are hindered by limited education, limited knowledge of digital information, words they do not know, and not knowing how to use the computer keyboard and mouse. The setbacks and frustration they face in learning to use the computer causes them to feel computer anxiety. This paper proposes a computer-learning-assisted system equipped with wrist-wearable devices to help elderly computer learners as they perform computer learning tasks. This system, named the WristEyesystem, can discern and analyze learners' attitudes, reactions, and behaviors as they participate in computer literacy classes. In the WristEyesystem, a kinematic sensor attached to a student's wrist can detect differences in wrist orientation and vertical acceleration and determine which learning computer operations are in process, i.e., directing the mouse, hitting the keyboard, idle, and random undirected movement of the mouse. Moreover, a remote backend server receives the detected signal from the wearable unit by a wireless sensor network and then analyzes the corresponding computer learning effectiveness to produce results in graphic and score form to an instructor who can use this information to better tailor his lessons and activities to the needs of the learners.

Experimental Study of Kinematic Activity of Left Ventricular Myocardium.

A method of invasive and non-invasive examination of epicardial motion was tested on domestic pigs (n=15). The invasive study employed a contact kinematic sensor of the epicardial wall motion, which determined the amplitude, velocity, and angular parameters of examined epicardial region. A non-invasive assessment of the epicardial kinematics based on echocardiographic 2D Velocity Vector Imaging and ECG recoding used to determine the heart electrical axis. The 4-chamber views were uses to analyze the longitudinal displacements of lateral wall of the left ventricle at the basal, mid, and apical segments. In addition, the radial displacements were determined in short axis projections at basal and apical segments as well as at the level of papillary muscles. The animals with normal or horizontal heart's electrical axis demonstrated similar displacement of the epicardium along X-axis and in longitudinal direction, while displacement along Y-axis corresponded to that in radial direction. The combined invasive and non-invasive approach in measurement of epicardium motion makes it possible to quantify the kinematic epicardial activity.

P137. Nigral and subthalamic neurostimulation modulate intermuscular coherence in Parkinson’s disease

Introduction Combined nigral and subthalamic stimulation is under consideration to modulate otherwise resistant axial motor symptoms, namely freezing of gait in idiopathic Parkinson’s disease (PD). The impact of subthalamic or nigral stimulation on neuromuscular motor integration however needs further characterization. Methods We used fully mobile EEG and EMG recordings, wireless kinematic sensors and video documentation to record patients during a gait cycle. Seven patients with deep brain stimulation were examined both during walking and standing after overnight withdrawal of dopaminergic medications. Recordings in different therapeutic conditions were obtained, i.e. ‘stimulation off’ (StimOff), ‘subthalamic stimulation on’ (STNOn), and ‘nigral stimulation on’ (SNrOn). Results The main finding was an increase of intermuscular coherence in the alpha and low beta bands during ‘walking’ in both STNOn and SNrOn. During ‘standing’ there was increased coherence in theta and alpha bands (‘STNOn’) and in the alpha and low beta bands (‘SNrOn’). Discussion Subthalamic and nigral stimulation increase intermuscular beta band coherence. Intermuscular beta band coherence may reflect increased corticospinal motor control from corticospinal beta drives to the spinal motor unit pool (Marsden et al., 2001; Weiss et al., 2012).

A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone.

Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional ECG holter is often inconvenient to carry because it has many electrodes attached to the chest and because it is heavy. This work proposes a wearable, low power context-aware ECG monitoring system integrated built-in kinetic sensors of the smartphone with a self-designed ECG sensor. The wearable ECG sensor is comprised of a fully integrated analog front-end (AFE), a commercial micro control unit (MCU), a secure digital (SD) card, and a Bluetooth module. The whole sensor is very small with a size of only 58 × 50 × 10 mm for wearable monitoring application due to the AFE design, and the total power dissipation in a full round of ECG acquisition is only 12.5 mW. With the help of built-in kinetic sensors of the smartphone, the proposed system can compute and recognize user’s physical activity, and thus provide context-aware information for the continuous ECG monitoring. The experimental results demonstrated the performance of proposed system in improving diagnosis accuracy for arrhythmias and identifying the most common abnormal ECG patterns in different activities. In conclusion, we provide a wearable, accurate and energy-efficient system for long-term and context-aware ECG monitoring without any extra cost on kinetic sensor design but with the help of the widespread smartphone.

Sensor positioning and experimental constraints influence estimates of local dynamic stability during repetitive spine movements

Application of non-linear dynamics analyses to study human movement has increased recently, which necessitates an understanding of how dependent measures may be influenced by experimental design and setup. Quantifying local dynamic stability for a multi-articulated structure such as the spine presents the possibility for estimates to be influenced by positioning of kinematic sensors used to measure spine angular kinematics. Oftentimes researchers will also choose to constrain the spine׳s movement by physically restraining the pelvis and/or using targets to control movement endpoints. Ten healthy participants were recruited, and asked to perform separate trials of 35 consecutive cycles of spine flexion under both constrained and unconstrained conditions. Electromagnetic sensors that measure three-dimensional angular orientations were positioned over the pelvis and the spinous processes of L3, L1, and T11. Using the pelvic sensor as a reference, each sensor location on the spine was used to obtain a different representation of the three-dimensional spine angular kinematics. Local dynamic stability of each kinematic time-series was determined by calculating the maximum finite-time Lyapunov exponent (λmax). Estimates for λmax were significantly lower (i.e. dynamically more stable) for spine kinematic data obtained from the L3 sensor than those obtained from kinematic data using either the L1 or T11 sensors. Likewise, λmax was lower when the movement was constrained. These results emphasize the importance of proper placement of instrumentation for quantifying local dynamic stability of spine kinematics and are especially relevant for repeated measures designs where data are obtained from the same individual on multiple days.

An immediate effect of custom-made ankle foot orthoses on postural stability in older adults

BackgroundFoot and ankle problems are highly prevalent fall risks in the elderly. Ankle foot orthoses designed to stabilize the foot and ankles have been studied within specific patient groups, but their efficacy with a less restrictive elderly population is unknown. This study investigated if custom-made ankle foot orthoses improve postural stability in older adults. MethodsThirty ambulatory older adults averaged 73 (standard deviation=6.5) years completed Romberg's balance (eyes-open/eyes-closed), functional reach, and Timed Up and Go tests while wearing validated kinematic sensors. Each test was completed in standardized shoes with and without bilateral orthoses. Additionally, barefoot trials were conducted for the Romberg's and functional reach tests. FindingsCompared to the barefoot and ‘shoes alone’ conditions, the orthoses reduced center of mass sway on average by 49.0% (P=0.087) and 40.7% (P=0.005) during eyes-open balance trials. The reduction was amplified during the eyes-closed trials with average reductions of 65.9% (P=0.000) and 47.8% (P=0.004), compared to barefoot and ‘shoes alone’ conditions. The orthoses did not limit functional reach distance nor timed-up and go completion times. However, the medial-lateral postural coordination while reaching was improved significantly with orthoses compared to barefoot (14.3%; P=0.030) and ‘shoes alone’ (13.5%; P=0.039) conditions. InterpretationAnkle foot orthoses reduced postural sway and improved lower extremity coordination in the elderly participants without limiting their ability to perform a standard activity of daily living. Additional studies are required to determine if these benefits are retained and subsequently translate into fewer falls.

A study of position independent algorithms for phone-based gait frequency detection.

Estimating gait frequency is an important component in the detection and diagnosis of various medical conditions. Smartphone-based kinematic sensors offer a window of opportunity in free-living gait frequency estimation. The main issue with smartphone-based gait frequency estimation algorithms is how to adjust for variations in orientation and location of the phone on the human body. While numerous algorithms have been implemented to account for these differences, little work has been done in comparing these algorithms. In this study, we compare various position independent algorithms to determine which are more suited to robust gait frequency estimation. Using sensor data collected from volunteers walking with a smartphone, we examine the effect of using three different time series with the magnitude, weighted sum, and closest vertical component algorithms described in the paper. We also test two different methods of extracting step frequency: time domain peak counting and spectral analysis. The results show that the choice of time series does not significantly affect the accuracy of frequency measurements. Furthermore, both time domain and spectral approaches show comparable results. However, time domain approaches are sensitive to false-positives while spectral approaches require a minimum set of repetitive measurements. Our study suggests a hybrid approach where both time-domain and spectral approaches be used together to complement each other's shortcomings.

Higher Crash and Near-Crash Rates in Teenaged Drivers With Lower Cortisol Response

Road traffic crashes are one of the leading causes of injury and death among teenagers worldwide. Better understanding of the individual pathways to driving risk may lead to better-targeted intervention in this vulnerable group. To examine the relationship between cortisol, a neurobiological marker of stress regulation linked to risky behavior, and driving risk. The Naturalistic Teenage Driving Study was designed to continuously monitor the driving behavior of teenagers by instrumenting vehicles with kinematic sensors, cameras, and a global positioning system. During 2006-2008, a community sample of 42 newly licensed 16-year-old volunteer participants in the United States was recruited and driving behavior monitored. It was hypothesized in teenagers that higher cortisol response to stress is associated with (1) lower crash and near-crash (CNC) rates during their first 18 months of licensure and (2) faster reduction in CNC rates over time. Participants' cortisol response during a stress-inducing task was assessed at baseline, followed by measurement of their involvement in CNCs and driving exposure during their first 18 months of licensure. Mixed-effect Poisson longitudinal regression models were used to examine the association between baseline cortisol response and CNC rates during the follow-up period. Participants with a higher baseline cortisol response had lower CNC rates during the follow-up period (exponential of the regression coefficient, 0.93; 95% CI, 0.88-0.98) and faster decrease in CNC rates over time (exponential of the regression coefficient, 0.98; 95%, CI, 0.96-0.99). Cortisol is a neurobiological marker associated with teenaged-driving risk. As in other problem-behavior fields, identification of an objective marker of teenaged-driving risk promises the development of more personalized intervention approaches.

Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs.

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project "Tremor" (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.

Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project “Tremor” (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.