We are designing a pediatric exoskeletal ankle robot (pediatric Anklebot) to promote gait habilitation in children with Cerebral Palsy (CP). Few studies have evaluated how much or whether the unilateral loading of a wearable exoskeleton may have the unwanted effect of altering significantly the gait. The purpose of this study was to evaluate whether adding masses up to 2.5 kg, the estimated overall added mass of the mentioned device, at the knee level alters the gait kinematics. Ten healthy children and eight children with CP, with light or mild gait impairment, walked wearing a knee brace with several masses. Gait parameters and lower-limb joint kinematics were analyzed with an optoelectronic system under six conditions: without brace (natural gait) and with masses placed at the knee level (0.5, 1.0, 1.5, 2.0, 2.5 kg). T-tests and repeated measures ANOVA tests were conducted in order to find noteworthy differences among the trial conditions and between loaded and unloaded legs. No statistically significant differences in gait parameters for both healthy children and children with CP were observed in the five ‘‘with added mass’’ conditions. We found significant differences among ‘‘natural gait’’ and ‘‘with added masses’’ conditions in knee flexion and hip extension angles for healthy children and in knee flexion angle for children with CP. This result can be interpreted as an effect of the mechanical constraint induced by the knee brace rather than the effect associated with load increase. The study demonstrates that the mechanical constraint induced by the brace has a measurable effect on the gait of healthy children and children with CP and that the added mass up to 2.5 kg does not alter the lower limb kinematics. This suggests that wearable devices weighing 25 N or less will not noticeably modify the gait patterns of the population examined here.

PLoS ONE 

In this paper we present the alpha-prototype of the WAKE-up, a wearable robotic device for the rehabilitation of locomotion of pediatric subjects with neurological diseases such as Cerebral Palsy. The WAKE-up is an active knee-ankle orthosis. It is composed of two robotic modules for the rehabilitation of knee and ankle, respectively. Each module can be utilized either alone or together with the other one. The working principle is based on series elastic actuators (SEA), i.e., dc motors equipped with a torsional spring mounted in series to avoid the direct connection of the actuator with the patient’s limb. A SEA permits the control of the force and the emulation of different orthoses with given value of stiffness. The torque transmission is achieved by a timing belt and it is mediated by a torsional spring. The experimental tests conducted on each modules confirmed a good precision of the spring deflection control (position error < 2°) and good overall performances of the force control obtained with the spring stiffness chosen at the design phase.

IEEE International Conference on Biomedical Robotics and Biomechatronics

Magnetic inertial measurement unit systems (MIMU) offer the potential to perform joint kinematics evaluation as an alternative to optoelectronic systems (OS). Several studies have reported the effect of indoor magnetic field disturbances on the MIMU’s heading output, even though the overall effect on the evaluation of lower limb joint kinematics is not yet fully explored.

The aim of the study is to assess the influence of indoor magnetic field distortion on gait analysis trials conducted with a commercial MIMU system. A healthy adult performed gait analysis sessions both indoors and outdoors. Data collected indoors were post-processed with and without a headingì correction methodology performed with OS at the start of the gait trial. The performance of theMIMU system is characterized in terms of indices, based on the mean value of lower limb joint angles and the associated ROM, quantifying the system repeatability. We find that the effects of magnetic field distortion, such as the one we experienced in our lab, were limited to the transverse plane of each joint and to the frontal plane of the ankle. Sagittal plane values, instead, showed sufficient repeatability moving from outdoors to indoors. Our findings provide indications to clinicians on MIMU performance in the measurement of lower limb kinematics.

Physiological Measurement  

This paper describes a novel functional body-to-sensor calibration procedure for inertial sensor-based gait analysis. The procedure is designed to be easily and autonomously performable by the subject, without the need for precise sensor positioning, or the performance of specific movements. The procedure consists in measuring the vertical axis during two static positions, and is not affected by magnetic field distortion. The procedure has been validated on ten healthy subjects using an optoelectronic system to measure the actual body-to-sensor rotation matrices. The effects of different sensor positions on each body segment, or different levels of subject inclination during the second static position of the procedure, resulted unnoticeable. The procedure showed accuracy and repeatability values less than 4 for each angle except for the ankle int–external rotation (9.7, 7.2). The results demonstrate the validity of the procedure, since they are comparable with those reported for the most-adopted protocols in gait analysis.

Measurement

In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

Sensors

In this paper we present and validate a methodology to avoid the training procedure of a classifier based on an Hidden Markov Model (HMM) for a real-time gait recognition of two or four phases, implemented to control pediatric active orthoses of lower limb. The new methodology consists in the identification of a set of standardized parameters, obtained by a data set of angular velocities of healthy subjects age-matched. Sagittal angular velocities of lower limbs of ten typically developed children (TD) and ten children with hemiplegia (HC) were acquired by means of the tri-axial gyroscope embedded into Magnetic Inertial Measurement Units (MIMU). The actual sequence of gait phases was captured through a set of four foot switches. The experimental protocol consists in two walking tasks on a treadmill set at 1.0 and 1.5 km/h. We used the Goodness (G) as parameter, computed from Receiver Operating Characteristic (ROC) space, to compare the results obtained by the new methodology with the ones obtained by the subject-specific training of HMM via the Baum-Welch Algorithm. Paired-sample t-tests have shown no significant statistically differences between the two procedures when the gait phase detection was performed with the gyroscopes placed on the foot. Conversely, significant differences were found in data gathered by means of gyroscopes placed on shank. Actually, data relative to both groups presented G values in the range of good/optimum classifier (i.e. G ≤ 0.3), with better performance for the two-phase classifier model. In conclusion, the novel methodology here proposed guarantees the possibility to omit the off-line subject-specific training procedure for gait phase detection and it can be easily implemented in the control algorithm of active orthoses.

IEEE International Symposium on Medical Measurements and Applications

Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs) represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to avoid the intra-subject one in two, four and six gait-phase models in pediatric subjects. The inter-subject procedure consists in the identification of a standardized parameter set to adapt the model to measurements. We tested the inter-subject procedure both on scalar and distributed classifiers. Ten healthy children and ten hemiplegic children, each equipped with two Inertial Measurement Units placed on shank and foot, were recruited. The sagittal component of angular velocity was recorded by gyroscopes while subjects performed four walking trials on a treadmill. The goodness of classifiers was evaluated with the Receiver Operating Characteristic. The results provided a goodness from good to optimum for all examined classifiers (0 < G < 0.6), with the best performance for the distributed classifier in two-phase recognition (G = 0.02). Differences were found among gait partitioning models, while no differences were found between training procedures with the exception of the shank classifier. Our results raise the possibility of avoiding subject-specific training in HMM for gait-phase recognition and its implementation to control exoskeletons for the pediatric population.

Sensors