Vincenzo Genovese

A step toward GPS/INS personal navigation systems: real-time assessment of gait by foot inertial sensing

In this paper, we develop a system for which applications in the field of personal navigation are planned. In the current version, the system embodies a Global Positioning System (GPS) receiver and an inertial measurement unit (IMU), composed of two dual-axis accelerometers and one single-axis gyro. The IMU is positioned at a subjects foot instep, and it is intended to produce estimates of some gait parameters, including stride length, stride time, and walking speed. Data from GPS and IMU are managed by a DSP-based control box. The computations performed by the DSP processor allow to detect subsequent foot contacts by a threshold-based method applied to gyro signal, and to reconstruct the trajectory of the foot instep by numerical strapdown integration. Features of human walking dynamics are incorporated in the algorithm to enhance the estimation accuracy against errors due to sensor noise and integration drift. All computations are performed by the DSP processor in real-time conditions. The foot sensor performance is assessed during outdoor level walking trials. The traveled distance estimated by inertial dead-reckoning is compared with the estimate produced by GPS in experimental conditions where GPS can be used as a reference source for accurate absolute positioning. Results show the remarkable accuracy achieved by foot inertial sensing.

A mobility aid for the support to walking and object transportation of people with motor impairments

In this paper we describe a motorised rollator which aims at supporting people with motor impairments while walking and transporting objects. The prototype presented here incorporates a few additional functions, including the force enhancement to the user in the direct control mode, and the ability to follow the user at a distance, with or without collision avoidance, in the follow-me mode. The user input device for the direct control mode is composed of bilateral grips, equipped with strain gage force sensors, whose output signals are used for motor speed control. The follow-me mode is built around a trilateration system based on ultrasonic and infrared sensing technologies; the system activates a user-worn transponder for person tracking. We discuss both the motivations behind the control modes of interest and the technical solutions we have devised for their implementation. The results of some preliminary experiments are also presented and discussed, to show the feasibility of the approach.

A low-cost, composite sensor array combining ultrasonic and infrared proximity sensors

In this paper we describe our approach to the design of proximity sensor arrays. Each sensing element of the proximity sensor array is a composite sensor, i.e. a sensor which is composed of an in-air ultrasonic rangefinder and an infrared detector. This sensor arrangement is capable, in principle, to achieve a perception of the explored objects that is not necessarily limited to their geometrical properties (size, shape and location relative to the sensor); possibly, the perception can be extended to other relevant features. In this paper, we show that, in principle, a sort of perception of the surface reflectance (the color) is achievable. The concept of the proposed sensor array spans a wide range of potential applications in flexible industrial automation, service robotics and autonomous mobility. The system described, in particular, is intended for providing an advanced wheelchair with the navigational capabilities required for improving the driving skills of disabled users.

Robot assistants : Applications and evolution

Abstract Service robots are machines designed to work not onlyfor human beings (like the industrial robot) butalso with human beings. In addition to challenging technical problems, the application of service robots in real-life situations requires to address intriguing issues concerning their interactions with humans. We are investigating both types of problems with reference to three different case studies and real research projects: a mobile robot system primarily aimed at the assistance of patients in hospitals and institutions (the “URMAD” project); a mobile robot system for the assistance to the disabled and the elderly at home (the “MOVAID” project); and a wheelchair incorporating a robot arm (the “IMMEDIATE” project). The paper discusses the design of the three robotic systems as well as their practical implementation and preliminary experimental results. Finally, the paper addresses recent results which could lead to the implementation of cybernetic prostheses, that is devices which would ultimately allow to achieve real intimate physical symbiosis between human beings and artificial robotic devices.

Online Decoding of Hidden Markov Models for Gait Event Detection Using Foot-Mounted Gyroscopes

In this paper, we present an approach to the online implementation of a gait event detector based on machine learning algorithms. Gait events were detected using a uniaxial gyro that measured the foot instep angular velocity in the sagittal plane to feed a four-state left-right hidden Markov model (HMM). The short-time Viterbi algorithm was used to overcome the limitation of the standard Viterbi algorithm, which does not allow the online decoding of hidden state sequences. Supervised learning of the HMM structure and validation with the leave-one-subject-out validation method were performed using treadmill gait reference data from an optical motion capture system. The four gait events were foot strike, flat foot (FF), heel off (HO), and toe off. The accuracy ranged, on average, from 45 ms (early detection, FF) to 35 ms (late detection, HO); the latency of detection was less than 100 ms for all gait events but the HO, where the probability that it was greater than 100 ms was 25%. Overground walking tests of the HMM-based gait event detector were also successfully performed.

Development of a closed-loop neural prosthesis for vestibular disorders

Vestibular disorders can cause severe problems including spatial disorientation, imbalance, nausea, visual blurring, and even cognitive deficits. The CLONS project is developing a closed-loop, sensory neural prosthesis to alleviate these symptoms [1]. In this article, we outline the different components necessary to develop this prosthetic. A short version of this work was presented in the NEUREL 2010 [1]. Conceptually, the prosthesis restores vestibular information based on inertial sensors rigidly affixed to the user. These sensors provide information about rotational velocity of the head; the prosthetic then transfers the information to the vestibular nerve via electrical stimulation. Here we present a project overview, development details, and summarize our progress in animal models and selected human volunteers.

Design and Evaluation of a new mechatronic platform for assessment and prevention of fall risks

BackgroundStudying the responses in human behaviour to external perturbations during daily motor tasks is of key importance for understanding mechanisms of balance control and for investigating the functional response of targeted subjects. Experimental platforms as far developed entail a low number of perturbations and, only in few cases, have been designed to measure variables used at run time to trigger events during a certain motor task.MethodsThis work introduces a new mechatronic device, named SENLY, that provides balance perturbations while subjects carry out daily motor tasks (e.g., walking, upright stance). SENLY mainly consists of two independently-controlled treadmills that destabilize balance by suddenly perturbing belts movements in the horizontal plane. It is also provided with force sensors, which can be used at run time to estimate the ground reaction forces and identify events along the gait cycle in order to trigger the platform perturbation. The paper also describes the customized procedures adopted to calibrate the platform and the first testing trials aimed at evaluating its performance.ResultsSENLY allows to measure both vertical ground reaction forces and their related location more precisely and more accurately than other platforms of the same size. Moreover, the platform kinematic and kinetic performance meets all required specifications, with a negligible influence of the instrumental noise.ConclusionA new perturbing platform able to reproduce different slipping paradigms while measuring GRFs at run time in order to enable the asynchronous triggering during the gait cycle was designed and developed. Calibration procedures and pilot tests show that SENLY allows to suitably estimate dynamical features of the load and to standardize experimental sessions, improving the efficacy of functional analysis.

A sensor fusion method for tracking vertical velocity and height based on inertial and barometric altimeter measurements.

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04–0.24 m/s; height RMSE was in the range 5–68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions.

A Stochastic Approach to Noise Modeling for Barometric Altimeters

The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions.

Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements

This paper investigates a fall detection system based on the integration of an inertial measurement unit with a barometric altimeter (BIMU). The vertical motion of the body part the BIMU was attached to was monitored on-line using a method that delivered drift-free estimates of the vertical velocity and estimates of the height change from the floor. The experimental study included activities of daily living of seven types and falls of five types, simulated by a cohort of 25 young healthy adults. The downward vertical velocity was thresholded at 1.38 m/s, yielding 80% sensitivity (SE), 100% specificity (SP) and a mean prior-to-impact time of 157 ms (range 40-300 ms). The soft falls, i.e., those with downward vertical velocity above 0.55 m/s and below 1.38 m/s were analyzed post-impact. Six fall detection methods, tuned to achieve 100% SE, were considered to include features of impact, change of posture and height, singularly or in association with one another. No single feature allowed for 100% SP. The detection accuracy marginally improved when the height change was considered in association with either the impact or the change of posture; the post-impact fall detection method that analyzed the impact and the change of posture together achieved 100% SP.