Leader: Fanny Ficuciello (UNINA); Other collaborator(s):
The goal is to identify personalised outcomes of robotic technology in rehabilitation of older multimorbid patients to inform spoke 8 activities in developing and testing robotic systems aimed at improving the quality and ability of movement of the elderly and frail people in the execution of daily tasks. Ad hoc control algorithms will be developed for elderly and frail subjects and testing protocols will be produced. The control strategies will be adapted according to the task, both for rehabilitation and for daily assistance, and will take into account the ascertained physical conditions of the patient as well as the physiological signals in real-time.
Brief description of the activities and of the intermediate results: In collaboration with Dr. Raffaele Dubbioso of the Department of Neuroscience of the Federico II University of Naples we draft an experimental protocol to be submitted to the Ethics Committee. Subject that will be involved are Amyotrophic Lateral Sclerosis (ALS) patients (of the Flail Arm and Flail Leg type), whom the doctor involved in the study is in charge of, and whose average age exceeds 65 years, associated with a group of healthy subjects as a reference. The analysis of the performance of these subjects will serve as a guideline to define the best outcomes of interest once robotic rehabilitation systems are applied.
Considerations from the analysis of the performance of a healthy subject using an end-effector rehabilitation system were integrated into the process of drafting and evaluating the setup and procedure to be followed in the experimental protocol. In particular, performance was assessed by taking into account the subject's muscle activations from information recorded by EMG sensors. In addition, specific algorithms were developed to control and assist in the completion of the tasks performed, taking into account the muscular-skeletal model of the upper limb and the subject's activation capabilities. The procedures to be followed in the experimental protocol agreed upon with Dr. Dubbioso were defined and drafted in full. In detail, the EMG sensors to be used, the muscles considered to be of greatest interest in assessing the motor skills of the test subjects, and the filtering techniques to be used on the raw data were chosen. In addition, an IMU sensor was also identified to be included within the setup, so as to derive information regarding the motion of the test subject's limb in terms of position, velocity, and acceleration. A specific video capture system was also included, with the purpose of mapping, through specific markers, the positions in space of all the joints of the limb and making a more complete assessment of the motion. In addition to the choice of setup, the metrics of greatest interest and relevance that will be extracted from these data to go on to numerically categorize the residual abilities of the patients under study were then also defined.
Drafting of the paper on HL-EU-Q6 validation for older people.
Main policy, industrial and scientific implications: These activities will be useful in identifying suitable robotic technology for specific diseases of the older people.
The previously drafted experimental protocol underwent corrections requested by the Ethics Committee. After the changes were implemented, the new version of the protocol was evaluated and officially approved in May. This allowed the experimental activities to begin in collaboration with the doctors who will follow the subjects involved in the study. The activities involved the collection of data from healthy subjects.
In parallel, algorithms were developed to analyze and extract metrics of interest from the collected data. In detail a vision algorithm was developed to analyze, segment and track markers used for mapping the movement of human joints while performing the task of interest. Segmentation of task sub-phases was also initiated using algorithms based on data extracted from the IMU sensors to allow for more detailed analysis. In addition, the exercises required to correctly record the Maximum Voluntary Contraction (MVC), a key parameter for meaningful analysis of EMG signals, were refined.
A literature review phase was initiated to identify the most effective control strategies to support the rehabilitation of the subjects. The integration of biosignals into the exoskeleton control loop was also investigated. With regard to passive exoskeletons, a study was initiated to identify the optimal configuration of auxiliary sensors to assess their effectiveness in subjects with functional limitations.
Additionally a new system, for evaluating the walking performance of the subjects involved in the experimental protocol developed, was integrated into the data acquisition setup. The system consists of two sensorised platforms for the measurement of the force applied to the ground by the subject under examination during step execution.
Finally, a bibliographic investigation phase has been initiated in order to define innovative quality indices, that take into account various bio-signals and parameters recorded during acquisitions. The final aim of this research is to extract a hyper-parameter indicative of the physiological state of the subject undergoing the specific physiotherapy assessment.
Data acquisition from healthy subjects has been ongoing, while initial data from patients with Amyotrophic lateral sclerosis (ALS), specifically in the early stages of the disease and presenting a Flail Arm phenotype, were also gathered. A passive upper limb exoskeleton was applied to the ALS subjects as part of this data collection process. The algorithms designed for data analysis were developed based on bibliographic research, and quality indices were derived to evaluate the data.
As the dataset grew with the inclusion of new ALS patients, the statistical analysis was updated, allowing for a more comprehensive comparison between healthy subjects and ALS patients. This expanded dataset enabled a more detailed evaluation of the differences and potential trends between the two groups.
Additionally, new algorithms were introduced to improve data analysis processes, and ongoing statistical analysis allowed us to refine the results. Preliminary results from these analyses have contributed to the preparation of a scientific paper.
Moreover, evaluations were carried out regarding the integration of a motor into one of the active exoskeleton structures within the experimental framework. These evaluations focused on potential design modifications aimed at enhancing the exoskeleton’s functionality. This work is being integrated into the broader effort to develop both passive and active exoskeleton systems for improved support in ALS patients.
In parallel, a literature search on the study of muscle synergies has begun in collaboration with a neurologist. The aim is to classify muscle synergies according to the stage of the disease and the muscles most involved. This approach is based on the idea that, as the disease progresses, patients tend to compensate for the loss of function in specific muscles through new neuromuscular activation strategies. Studying these residual synergies could provide crucial information on the compensatory mechanisms that are activated in response to motor deterioration.
During the period October–December 2024, advancements were made in both data collection and analysis to deepen the understanding of motor impairment and muscle fatigue in ALS patients and healthy controls. Data collection efforts focused on ALS patients with the Flail Arm (FA) phenotype, including reassessments of previously monitored patients to track the progression of muscle weakness. Concurrently, data collection from healthy subjects provided baseline information to support comparative analyses.
The experimental protocol was refined to enhance the evaluation of muscle fatigue. Based on preliminary observations, adjustments were made to the structure and execution of exercises to ensure tasks more effectively captured fatigue-related changes in muscle activity. These modifications aimed to provide more accurate and detailed fatigue metrics, which are critical for understanding motor impairment progression in ALS patients.
In parallel, data analysis was conducted on surface electromyography (sEMG) signals, focusing on extracting muscle synergies. Various methodologies were explored to identify the optimal number of synergies, a key parameter for assessing motor control capacity and its degradation in pathological conditions.
Additional physiological signals were integrated to enrich the experimental framework further, including electrocardiographic (ECG) data and galvanic skin response (GSR). These enhancements aim to provide a comprehensive perspective on muscle fatigue by correlating it with broader physiological responses.
Dissemination