Robotic rehabilitation systems typically rely on kinematic data to assess exercise execution, focusing on pose accuracy without evaluating the physical impact on the patient. This paper explores the integration of physiological monitoring into an automated planning system for robot-aided rehabilitation. Specifically, we assess whether Heart Rate (HR) can provide meaningful feedback on the experimented physical workload while executing physical tasks. Ten healthy participants completed a session of 10 exercises, with cumulative exercise intensity (Σ ) and HR recorded. A Pearson correlation analysis revealed an average strong correlation of 0.63 across all participants, indicating that HR reliably reflects physical efort. The results suggest that incorporating HR feedback allows the system to dynamically adjust exercise intensity in real-time, ensuring that patients are neither over- nor under-challenged.
Robotic systems have become essential tools in rehabilitation, assisting with physical recovery by
delivering personalized, repeatable, and adaptive interventions. These systems include exoskeletons [
In current robotic rehabilitation platforms, automated planning systems can select and sequence
exercises based on clinical goals [
Nevertheless, focusing solely on kinematic data has significant limitations, as it fails to capture the
overall physical impact of the rehabilitation session. While kinematic monitoring efectively ensures
that individual movements are executed correctly, it does not account for the cumulative physical
efort required throughout the session. This information gap can lead to exercises that either under- or
over-challenge the patient, potentially slowing their rehabilitation progress. In contrast, incorporating
physiological monitoring allows for tracking the patient’s physical workload during the execution of
the exercises [
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Therefore, this paper aims to evaluate whether the integration of physiological monitoring into a multimodal patient monitoring system can provide valuable insights into the patient’s physical workload. These insights can be leveraged by an automated planning-based robot-aided rehabilitation system to more efectively tailor therapy sessions, ensuring that the prescribed exercises align with the patient’s actual physical efort and overall exertion. By combining both kinematic and physiological data, the system has the potential to deliver a more adaptive and personalized rehabilitation experience.
The block diagram reported in Fig. 1 illustrates the structure of a multimodal rehabilitation system that integrates both kinematic and physiological monitoring to optimize exercise sessions tailored to individual patients.
Session inputs provide the initial parameters for the rehabilitation session, including clinical goals and
desired intensity. These inputs form the basis for personalizing the session to meet specific therapeutic
objectives. Based on these inputs, the Session Planner generates an optimized sequence of exercises
using automatic planning techniques. Its role is to ensure that the therapeutic session is in line with the
predefined clinical objectives by dynamically adjusting the exercise sequence if the monitored physical
exertion of the patient deviates from the planned intensity. This module stores a list of exercises labeled
with the intensity level ranging in [
The Session Manager monitors the execution of the planned exercises, interacting with both the patient and the monitoring systems to ensure smooth progression throughout the session. It manages the session in real-time by controlling the Feedback system according to the data collected from the monitoring systems. Specifically, it compares the prescribed task with the patient’s actual performance and provides the task completion percentage as feedback. Once the task is performed for the required duration, the system advances to the next exercise, maintaining a structured flow throughout the rehabilitation session.
At the heart of the system is Multimodal Monitoring, which consists of two key components: Physiological Monitoring and Kinematic Monitoring. The former tracks the physiological responses of the patient which provides essential, slow-varying feedback on the physical efort required by the patient during the execution of the exercise. This information allows the system to adjust the sequence and intensity of the exercises to ensure that the prescribed levels of physical exertion match the clinical goals. Among the measurable physiological metrics, HR is a reliable indicator of exertion, ofering insights into the patient’s response to the exercises. Wrist-worn devices, such as smartwatches, are ideal for continuous HR tracking due to their reliability and non-intrusive design.
The latter captures the movements of the patient in real-time, ensuring that the exercises are correctly performed, for the correct amount of time. This data helps to manage the therapy session in real-time providing immediate feedback on task performance and facilitating the correct session flow. Whenever the patient executes the assigned task for a determined amount of time correctly, the Session Manager administers the subsequent exercise. 2.1. Experimental Evaluation In this experiment, 10 healthy participants (31.4 ± 5.1 years old, 9 males and 1 female) were recruited. The experimental protocol was designed to assess the efect of various rehabilitation exercise plans on the participants’ physiological responses, with a particular focus on HR as an indicator of physical efort. Each participant completed a rehabilitation session consisting of 10 exercises, selected from the database managed by the Session Planner. Each exercise was performed for 30 seconds. Figure 2 shows a representative participant performing the exercises while being monitored by a multimodal monitoring system. More in detail, the Garmin Vivosmart 4 wristband was used to monitor HR without disrupting the patient’s comfort or movement during rehabilitation sessions, collecting data at 1 Hz. The TIAGo robot’s head-mounted camera (ASUS Xtion) and the Mediapipe pose algorithm allow for kinematics monitoring. They were used to capture and track the coordinates of the total body anatomical landmarks at 30 Hz. A classification model, trained on data from four participants across 23 exercises, extracted from the ”PhysioTherapy eXercises” database [12] due to their capacity to elicit a range of intensities and body districts, utilizes a Support Vector Machine with a radial basis function kernel to identify the task currently performed by the participant running at 15 Hz. where ( ) represents the intensity of the -th exercise. This cumulative measure reflects the progressive workload imposed by the exercises throughout the session.
We then computed the Pearson correlation coeficient between Σ and the HR data collected during the session to assess how closely the actual physical workload experienced by the participants is aligned with the planned intensity of the exercises. By examining this relationship, we aimed to determine whether physiological monitoring can provide reliable feedback on the intensity of the administered exercises, ofering valuable insights for adjusting and optimizing future rehabilitation sessions. The statistical significance level was set at -value= 0.05.
=1 Σ = ∑ [ ( )] (1)
This study demonstrated how integrating physiological monitoring can provide crucial insights into assessing the actual physical workload during robot-aided rehabilitation sessions. The results showed a significant correlation between the cumulative intensity of the prescribed exercises and the HR for most participants. On average, a correlation of 0.63 was found across all participants, indicating a strong relationship between exercise intensity and physiological response. This reinforces the importance of incorporating physiological measurements into automated planning systems, moving beyond the sole reliance on kinematic data.
The ability to use real-time HR feedback allows the system to dynamically re-plan rehabilitation sessions, adjusting exercise intensity to avoid overloading or under-challenging the patient with respect to the assigned clinical goals. This flexibility introduces a new level of personalization in treatment, improving the efectiveness of therapy and ensuring better alignment with the patient’s needs.
Future eforts will focus on exploring combinations of physiological metrics to assess both physical and cognitive workload, providing a more comprehensive understanding of patient exertion during rehabilitation. Additionally, this higher-level feedback will be integrated into the session planner to tailor rehabilitation sessions according to specific therapist requirements. Validation of this approach will be necessary to ensure its capability to deliver personalized therapeutic strategy.
This work was supported by the Italian Ministry of Research, under the complementary actions to the NRRP “Fit4MedRob - Fit for Medical Robotics” Grant PNC0000007, (CUP: B53C22006990001). [12] J. Glinsky, L. Harvey, C. Sherrington, O. Katalinic, www. physiotherapyexercises. com–new exercises and features to help physiotherapists prescribe home exercise programs, Physiotherapy 101 (2015) e1381.