WO2022146271A1 - Système d'évaluation de participation basé sur l'évaluation multimodale de réponses d'utilisateurs pour la rééducation des membres supérieurs - Google Patents

Système d'évaluation de participation basé sur l'évaluation multimodale de réponses d'utilisateurs pour la rééducation des membres supérieurs Download PDF

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WO2022146271A1
WO2022146271A1 PCT/TR2020/051505 TR2020051505W WO2022146271A1 WO 2022146271 A1 WO2022146271 A1 WO 2022146271A1 TR 2020051505 W TR2020051505 W TR 2020051505W WO 2022146271 A1 WO2022146271 A1 WO 2022146271A1
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patient
therapy
performance
exercises
tiredness
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PCT/TR2020/051505
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Erkan ODEMIS
Cabbar Veysel BAYSAL
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Cukurova Universitesi Rektorlugu
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    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
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Definitions

  • the invention relates to a system including a therapy exercise patient participation assessment method, which evaluates the participation and performance of patients during the upper extremity rehabilitation exercises based on a multimodal sensor fusion formed by the trajectory tracking error signal and the patient's physiological responses (heart rate and skin conductance) and adjusts difficulty levels of therapy exercises according to the patient's participation.
  • Stroke and Spinal Cord Injury are some of the most common causes of neuromuscular disorders, which have a severe impact on the ability of patients to achieve activities of daily living (ADL).
  • Physiotherapy and rehabilitation are widely used methods for treating patients with neuromuscular disorders.
  • rehabilitation exercises are performed with therapists. Still, this method has some disadvantages, such as not enough time being spent with each patient or performance reduction of therapists in exercises due to overloading.
  • performing exercises with robotic devices have appeared as a new approach to overcoming the disadvantages of conventional therapy.
  • the patient's active participation in the exercises is crucial in obtaining maximum functional outputs and improving neural plasticity.
  • the Assist-as-Needed (AAN) paradigm has emerged to ensure patients' active and voluntary participation [6]
  • AAN Assist-as-Needed
  • the robotic assistance provided to patients is determined based on the patient's performance.
  • therapy tasks and their difficulty levels are adjusted according to the patient's performance to ensure the exercises are challenging enough for the patient.
  • robotic assistance is applied when the patients diverge from reference trajectories or could not complete the desired therapy tasks. Thereby, it is aimed to enable patients to participate more actively in therapy exercises and to prevent passive training therapies that do not give much functional output.
  • the core of the AAN strategies is the patient performance evaluation method, which is the base for determining the robotic assistance provided to the patient and adjusting the therapy tasks.
  • patient performance evaluation method which is the base for determining the robotic assistance provided to the patient and adjusting the therapy tasks.
  • the existing methods in the literature are based on either some specific device designs, or some certain therapy tasks.
  • some performance evaluation methods ignore the patient's changing capabilities during the exercises. Krebs et al. first implemented a performance-based progressive robotic therapy, where the patient’s performance was estimated using the patient’s active force and motion accuracy. The stiffness of the system has been determined by the patient’s performance of the last reaching movement. Papaleo et al.
  • Leconte and Ronsse proposed a performance-based assistive strategy that measured the movement performance based on three features (smoothness, velocity, and amplitude) of the patient's motion by using an adaptive oscillator on rhythmic circular arm exercises. Evaluation of movement performance with this approach is suitable only on rhythmic circular therapy tasks.
  • Pehlivan et al. introduced a minimal assist-as-needed (mAAN) strategy, which relied on patients' sensorless force estimation by using a Kalman filter.
  • Chen et al. simulated patient interaction forces with a dynamic human arm model and real-time measurement of patient-exerted torques.
  • a significant shortcoming of both approaches was that the estimation of the patient’s capabilities had been based on the dynamic model of the robotic devices. As the robotic device's mechanical structure becomes complex, it is challenging to estimate patient performance precisely.
  • Carmichael and Liu proposed a model-based AAN structure that estimated the muscular capability of the patient by using a musculoskeletal model.
  • a Task model (TM) calculates the strength required to perform desired upper limb tasks
  • a Strength Model (SM) estimates the patient’s strength capability on a muscular level by using a musculoskeletal model.
  • Patient capabilities depend on other factors such as tiredness, joint stability, comfort, and the ability to coordinate movements. In this approach, these other factors have been ignored. Also, musculoskeletal model parameters have not been adjusted according to the patient.
  • Physiological responses are good references for evaluating the patients' physical activity and emotional states during the rehabilitation exercises.
  • stress level detection or emotion recognition based on physiological responses were implemented by researchers.
  • the research studies using physiological signals assessed patients' mental status like valance and arousal based on these signals.
  • the researchers adjusted the therapy difficulties according to patients' these psychological states.
  • none of the previous researches has been used physiological responses to evaluate the patient's performance and therapy engagement.
  • new systems and/or methods have been proposed for motion analysis during therapy exercises. Nevertheless, the patient's therapy performance was not evaluated in any of these motion analysis systems.
  • the invention relates to a system that including a therapy exercise patient participation assessment method, which evaluates the participation and performance of patients during the upper extremity rehabilitation exercises based on a multimodal sensor fusion formed by the trajectory tracking error signal and the patient's physiological responses (heart rate and skin conductance) and adjusts difficulty levels of therapy exercises according to the patient's participation.
  • the method evaluates the therapy performances of patients independently from any therapy tasks or device designs.
  • the developed system also evaluates the patient's tiredness and slacking, which are substantial factors affecting therapy performance. This method presents a low-cost system and can be applied in any kind of rehabilitation exercises. Since the system evaluates patient performance independently from any device design, it can also be used for performance evaluation in conventional therapy and sports exercises. LIST OF FIGURES
  • the system of the invention contains software that applies a therapy exercise patient participation assessment method which evaluates the participation and performance of patients during upper extremity rehabilitation exercises based on a multimodal sensor fusion formed by the trajectory tracking error signal and the patient's physiological responses (heart rate and skin conductance).
  • the method evaluates the therapy performances of patients independently from any therapy tasks or device designs.
  • the developed system also evaluates the patient's tiredness and slacking, which are substantial factors affecting therapy performance.
  • Physiological responses are good indicators for evaluating the patients' physical activities during the rehabilitation exercises.
  • HR and skin conductance signals would be a robust method for estimation of the physical and mental workload of a person during virtual rehabilitation tasks.
  • the HR and skin conductance signals increase with high physical activity and decrease with the fall of physical effort during the therapy exercises. Therefore, skin conductance, heart rate signals, and alteration of these signals (increase or decrease) are employed in the proposed method for evaluating the physical effort and voluntary participation of the patient in the therapy exercises.
  • the desired trajectory tracking error signal which expresses the difference between the desired therapy task and the patient's position during rehabilitation exercises, is a driving signal for motor learning. Therefore, we combined the trajectory tracking error signal and physiological responses for estimating the patient's performance during therapy to improve motor learning.
  • the proposed method can be considered a complex system comprising five main parts; multimodal sensory subsystem (A), upper limb kinematic module (B), Patient Response Estimator Subsystem (PRES) (C), therapy task management module (D), and Graphical User Interface (GUI) (E).
  • A multimodal sensory subsystem
  • B upper limb kinematic module
  • PRES Patient Response Estimator Subsystem
  • D therapy task management module
  • GUI Graphical User Interface
  • GUI Matlab Graphical User Interface
  • the multimodal sensor subsystem (A) contains two sensory data; inertial measurement unit (IMU) sensors and physiological signals sensors. All the sensor data is transferred to the target PC via serial communication with a 10 Hz sampling rate and processed in a MATLAB® / Simulink model. For measuring the upper arm joints' angles and evaluating the desired trajectory tracking performance of the patient during therapy, two IMU sensors are used. These sensors are mounted on the patient's right arm using cuffs, one for the upper arm and one for the forearm during rehabilitation exercises.
  • IMU inertial measurement unit
  • the quaternion outputs of IMU sensors were first transformed into upper extremity joint angles. Then, using the upper extremity kinematic module (B), the patient's arm movements during the exercises were estimated.
  • the trajectory tracking error signal is achieved by comparing the desired therapy task, determined by the therapy task management module, and the patient's arm movement obtained using the upper limb kinematic module.
  • Heart rate indicates the number of heartbeats in one minute and increases depending on the sympathetic nervous system's activity.
  • the heart rate is measured by using the Heart Rate sensor, which uses the photoplethysmography (PPG) technique.
  • PPG is a noninvasive and low-cost optical technique applied to detect blood volume changes in tissues affected by heartbeats.
  • the heart rate sensor is located on the subject's left-hand index finger.
  • Skin conductivity (or galvanic skin response (GSR)) describes the electrical conductivity of the skin, which varies depending on the secretions of the sweat glands and represents the activity of the sympathetic nervous system.
  • the selected sensor for measuring the skin conductance is the GSR sensor. Electrodes of the sensor are placed on the middle finger and thumb of the subject's left hand. GSR sensor measures the microvolts between these fingers.
  • human skin resistance (in ohm) has been calculated using Equation 1. Then the human skin resistance has been converted to skin conductance. During the exercises, the subjects were asked not to move their left hands in which the HR and GSR sensors were attached for not affecting the measurements.
  • the patient's therapy performance, tiredness, and slacking during the rehabilitation exercises are assessed by PRES, using a Fuzzy Inference System (FIS).
  • FIS is the process of mapping from a given input to an output using the fuzzy set theory and fuzzy logic.
  • the FIS has been applied to a wide variety of problems such as robotic, control, biomechanics and is also used for medical applications successfully.
  • the FIS usually are implemented in Mamdani and Sugeno methods. In this research, the Mamdani method was selected due to its easy-to-apply intuitive structure and compatibility with human inputs. Also, the Mamdani method can be implemented with observations and is suitable for the guidance of the physiotherapists.
  • the FIS contains three central units; fuzzification, decision-making, and defuzzification.
  • Fuzzification is a mathematical operation for converting an element in the universe of discourse into a fuzzy set membership value.
  • the fuzzification process receives the elements x,y e X and produces the membership degrees
  • the Decision-making unit achieves mapping of a given input to an output using membership functions, logical operations, and predefined IF- THEN rules. This process maps the fuzzified inputs to the rule base and produces a fuzzified output for each rule.
  • Defuzzification is a mathematical process used to convert a fuzzy set to a real number.
  • the defuzzification unit transforms the fuzzy results of the interface into output variables.
  • the defuzzification process used in this work is the centroid of the area (CoA) method.
  • the designed FIS takes the HR, GSR, the desired trajectory tracking error, and alterations of these signals during the therapy exercises as inputs. Since physiological signals can vary according to environmental factors and from person to person, changes in these signals during the exercises (increase or decrease) are considered in the patient's performance and tiredness evaluation. Alteration in the tracking error signal is used for tiredness evaluation only.
  • the proposed FIS has three outputs; Performance, Tiredness, and Slacking, as shown in Figure 2. In the figure, Ae(t), AHR and AGSR represent the changes in trajectory tracking error, HR, and GSR signals, respectively.
  • All input and output variables of the FIS are defined by membership (MS) functions
  • Membership functions of the FIS have been determined experimentally and characterized based on the researches in literature. For deciding the alterations, HR, GSR, and the tracking error signals are averaged every five seconds of simulation time and compared to their last mean values. If these signals increase or decrease, they take the "High” or “Low” value, respectively. The medium value is used for unchanged signals.
  • the membership functions of all the inputs and outputs of the FIS are given in Figure 3-7.
  • a fuzzy rule-based module with a total of 58 IF-THEN rules has been defined. All the rules have been designated experimentally. Some examples of the rules are given in Table 1 .
  • HR and GSR signals are good references for evaluating the patient's physical activity during the virtual rehabilitation tasks. Thence in the proposed method, performance evaluation is carried out using the tracking error signal with alterations in the HR and GSR responses. For instance, if the HR and GSR signals are decreasing and the tracking error value is "Low", this indicates that the patient has successfully performed the exercise; the exercise is not enough challenging for the patient. The performance of the patient is considered "High” for these conditions, Suppose the HR and GSR signals are increasing, and the tracking error is also high.
  • this condition is considered the patient could not perform the exercise despite his/her effort and performance will take "Low” value.
  • Patients' performance may change momentarily during exercises. These instant performance variations should not be neglected. However, these variations can cause therapy task difficulty levels to change frequently when therapy tasks are determined by patient performance. Therefore, to not neglect the instantaneous performance variations of the patients and prevent the therapy task difficulty levels from changing too often due to these variations, the performance assessment was implemented by taking the mean of the PRES's performance output values every twenty seconds of simulation time. If this mean performance value is more than 0.7, the therapy task's difficulty level is increased; if it is less than 0.55, it is decreased.
  • Upper and lower boundaries of the performance assessment in which the default values are 0.7 and 0.55, have been specified experimentally. These boundaries could be changed by using the sliders on the GUI screen to make the exercises easier or more difficult based on the patient's impairment level and performance.
  • HR and GSR signals are accurate indicators for predicting a person's tiredness. Therefore, tiredness evaluation is achieved based on HR, GSR, and the tracking error signal. For instance, if the patient's HR and GSR signals are high, and the tracking error signal is medium, the patient's tiredness is evaluated as "MidHigh", if the tracking error signal is high, then tiredness is considered as "High”. For improving the tiredness estimation, the tracking error signal's alteration was also used in the assessment.
  • Tiredness assessment is performed by averaging the tiredness output values of the PRES for every ten seconds of simulation time. By the averaging operation, momentary changes in HR and GSR signals are prevented from affecting tiredness assessment. If the patient's mean tiredness value is more than 0.7, the therapy difficulty level is reduced, regardless of performance evaluation.
  • the boundary of the tiredness assessment has been specified experimentally. This boundary could be changed by using the sliders on the GUI screen to make the exercises easier or more difficult based on the patient's impairment level and performance.
  • slacking motor control behavior that will allow robotic devices to take control of the tasks.
  • the slacking assessment is also considered in this work.
  • the slacking evaluation is achieved in a reverse manner to the tiredness evaluation. If the patient's HR and GSR signals are low, and the tracking error signal is high, the patient's slacking is evaluated as "High”. If the slacking value is estimated at more than 0.7, a warning message for the patient appears on the GUI screen. This value has been specified experimentally and could be changed by using the sliders on the GUI screen.
  • the implemented algorithms for performance, tiredness, and slacking assessment is given in Figure 8-10.
  • the proposed method offers a low-cost, easy to apply, and noninvasive solution for the performance assessments of the patients during the rehabilitation exercises, which is crucial to increase the functional outputs received from therapy.
  • This system is suitable for use in-home, clinical treatment environments, and telerehabilitation applications. Since the system evaluates patient performance independently from any device design, it can also be used for performance evaluation in conventional therapy and sports exercises.
  • the efficacy of the proposed method was tested on seven healthy subjects experimentally.
  • the subjects carried out the exercises for ten to twelve minutes, depending on their performance.
  • the performance assessment of all the subjects is given in Figure 11-17.
  • vertical lines indicate the 20-second intervals that the performance of subjects was evaluated.
  • the variations in task difficulty levels for all the subjects are given in Figure 18.
  • the exercises are indicated by numbers. When a subject completes the three difficulty levels of a therapy task, they move on to the next task.
  • the transition between therapy tasks and difficulty levels of these tasks has been carried out by the therapy task management module according to the PRES's performance and tiredness outputs.
  • the instant high trajectory tracking error signals of the subjects during the therapy usually arise from the adaptation period to the new therapy task or exercise level.
  • patients' performance can change momentarily, as seen in performance assessments of all the subjects given in Figure 11 -17.
  • carrying out patient performance evaluation during a certain exercise period provides a more accurate assessment.
  • the system detected tiredness. Due to tiredness detection, the exercise level was reduced twice during this subject's experiment. Tiredness evaluation of this subject is also given in Figure 22. In this figure, vertical lines indicate the 10-second intervals that the tiredness of the subject was evaluated.
  • the seventh subject's physiological signals are analyzed, it is seen that the GSR and especially the HR signal increase excessively during the exercises. The main reason for these increases is the excessive effort exerted by the subject to perform the tasks; therefore, a tiredness detection has occurred.
  • the fifth subject's GSR signal exceeded the medium level (20 pS) during the experiment. However, this subject's HR signal remained stable throughout the experiment. Therefore, no tiredness determination has been performed for this subject.
  • the points at which patients begin to perform the exercises with simulated post-stroke behavior are indicated as points A and B on the physiological responses of subjects 1 and 2, respectively.
  • points A and B the physiological responses of subjects 1 and 2, respectively.
  • the performance and slacking assessments of the subjects during these experiments are given in Fig. 27-30.
  • the trajectory tracking error signals of both subjects increased during the simulated post-stroke behavior.
  • the subjects' performances have been evaluated as low, and the developed system has decreased the exercise levels.
  • the patient's pulse and skin conductance physiological responses are measured using HR and GSR sensors.
  • Upper extremity joint angles are measured using inertial measurement unit (IMU) sensors.
  • IMU inertial measurement unit
  • All the sensor data is transferred to the target PC via serial communication with a 10 Hz sampling rate and processed in a MATLAB® I Simulink model.
  • the upper limb kinematic module (B) estimates the patient's arm movements during rehabilitation exercises using measured joint angles.
  • the trajectory tracking error signal is achieved by comparing the desired therapy task, determined by the therapy task management module (D), and the patient's arm movement.
  • the PRES (C) evaluates the patient's therapy performance, tiredness, and slacking using physiological responses and trajectory tracking error signal based on a Fuzzy Inference System. Therapy tasks and difficulty levels are adjusted by the therapy task management module (D) based on the patient’s performance and tiredness.
  • Rehabilitation exercises are performed using a Graphical User Interface (GUI) (E) screen.
  • GUI Graphical User Interface

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Abstract

L'invention concerne un système qui comprend un procédé d'évaluation de participation d'un patient à des exercices thérapeutiques, qui évalue la participation et les performances de patients pendant les exercices de rééducation des extrémités supérieures sur la base d'une fusion multimodale de capteurs formée par le signal d'erreur de suivi de trajectoire et les réponses physiologiques du patient (rythme cardiaque et conductance de la peau) et qui règle les niveaux de difficulté d'exercices thérapeutiques en fonction de la participation du patient.
PCT/TR2020/051505 2020-12-31 2020-12-31 Système d'évaluation de participation basé sur l'évaluation multimodale de réponses d'utilisateurs pour la rééducation des membres supérieurs WO2022146271A1 (fr)

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TR2020/22809A TR202022809A2 (tr) 2020-12-31 2020-12-31 Üst ekstremi̇te rehabi̇li̇tasyonu i̇çi̇n kullanici yanitlarinin multi̇modal değerlendi̇rmesi̇ne dayali bi̇r katilim değerlendi̇rme si̇stemi̇
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160202755A1 (en) * 2013-09-17 2016-07-14 Medibotics Llc Sensor Array Spanning Multiple Radial Quadrants to Measure Body Joint Movement
US20170202724A1 (en) * 2013-12-09 2017-07-20 President And Fellows Of Harvard College Assistive Flexible Suits, Flexible Suit Systems, and Methods for Making and Control Thereof to Assist Human Mobility
US20170258390A1 (en) * 2016-02-12 2017-09-14 Newton Howard Early Detection Of Neurodegenerative Disease
US20190247650A1 (en) * 2018-02-14 2019-08-15 Bao Tran Systems and methods for augmenting human muscle controls
US20200297279A1 (en) * 2019-03-20 2020-09-24 Cipher Skin Garment sleeve providing biometric monitoring

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160202755A1 (en) * 2013-09-17 2016-07-14 Medibotics Llc Sensor Array Spanning Multiple Radial Quadrants to Measure Body Joint Movement
US20170202724A1 (en) * 2013-12-09 2017-07-20 President And Fellows Of Harvard College Assistive Flexible Suits, Flexible Suit Systems, and Methods for Making and Control Thereof to Assist Human Mobility
US20170258390A1 (en) * 2016-02-12 2017-09-14 Newton Howard Early Detection Of Neurodegenerative Disease
US20190247650A1 (en) * 2018-02-14 2019-08-15 Bao Tran Systems and methods for augmenting human muscle controls
US20200297279A1 (en) * 2019-03-20 2020-09-24 Cipher Skin Garment sleeve providing biometric monitoring

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