Marco Donati

Automated detection of gait initiation and termination using wearable sensors

This paper presents algorithms for detection of gait initiation and termination using wearable inertial measurement units and pressure-sensitive insoles. Body joint angles, joint angular velocities, ground reaction force and center of plantar pressure of each foot are obtained from these sensors and input into supervised machine learning algorithms. The proposed initiation detection method recognizes two events: gait onset (an anticipatory movement preceding foot lifting) and toe-off. The termination detection algorithm segments gait into steps, measures the signals over a buffer at the beginning of each step, and determines whether this measurement belongs to the final step. The approach is validated with 10 subjects at two gait speeds, using within-subject and subject-independent cross-validation. Results show that gait initiation can be detected timely and accurately, with few errors in the case of within-subject cross-validation and overall good performance in subject-independent cross-validation. Gait termination can be predicted in over 80% of trials well before the subject comes to a complete stop. Results also show that the two sensor types are equivalent in predicting gait initiation while inertial measurement units are generally superior in predicting gait termination. Potential use of the algorithms is foreseen primarily with assistive devices such as prostheses and exoskeletons.

Development of an in-shoe pressure-sensitive device for gait analysis

In this work, we present the development of an in-shoe device to monitor plantar pressure distribution for gait analysis. The device consists in a matrix of 64 sensitive elements, integrated with in-shoe electronics and battery which provide an high-frequency data acquisition, wireless transmission and an average autonomy of 7 hours in continuous working mode. The device is presented along with its experimental characterization and a preliminary validation on a healthy subject.

Providing Time-Discrete Gait Information by Wearable Feedback Apparatus for Lower-Limb Amputees: Usability and Functional Validation

Here we describe a novel wearable feedback apparatus for lower-limb amputees. The system is based on three modules: a pressure-sensitive insole for the measurement of the plantar pressure distribution under the prosthetic foot during gait, a computing unit for data processing and gait segmentation, and a set of vibrating elements placed on the thigh skin. The feedback strategy relies on the detection of specific gait-phase transitions of the amputated leg. Vibrating elements are activated in a time-discrete manner, simultaneously with the occurrence of the detected gait-phase transitions. Usability and effectiveness of the apparatus were successfully assessed through an experimental validation involving ten healthy volunteers.

Development of gait segmentation methods for wearable foot pressure sensors

We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

Bio-inspired mechanical design of a tendon-driven dexterous prosthetic hand

This paper presents the preliminary design of a new dexterous upper-limb prosthesis provided with a novel anthropomorphic hand, a compact wrist based on bevel gears and a modular forearm able to cover different levels of upper-limb amputations. The hand has 20 DoFs and 11 motors, with a dexterous three fingered subsystem composed by a fully actuated thumb, and an hybrid index and middle fingers to enable dexterous manipulation and enhance grasp performance.

Development of an Experimental Set-Up for Providing Lower-Limb Amputees with an Augmenting Feedback *

We present a proprioceptive feedback system for lower-limb amputees based on vibratory stimulation applied on the thigh. The system should be integrated in the prosthesis for overcoming the missing proprioceptive information from the amputated foot sole. It acquires data from one pressure-sensitive insole, inserted in the shoe of the amputated foot, and elaborates the acquired information for the real-time detection of specific gait-phase transitions. On the basis of the recognized transition, one of the vibrators positioned on the amputees thigh is activated.