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The objective of this study is to determine what are the most robust parameters for the measurement of emotional states in users suffering from cerebral palsy. Users have different ages (adults and children) with different capacities. Measures will be taken in different contexts where users will do several tasks pleasant and unpleasant. Some of the tasks involve physical activity, which must be taken into account due to the possible disturbance that it can introduce in the measures taken.
It is intended to detect states of demotivation, fatigue, or physical or emotional stress. For this, we will use signals of two types: physiological measurements and inertial sensors. The handicap we find is that the subjects have difficulties expressing and recognizing emotional states, which rules out the use of a self-assessment test to contrast the measures taken. This makes us turn to their caregivers or family members or alternatively or in a complementary way to take measurements in contexts or situations of daily life where the emotional state induced in the subject is known.
Once the parameters were established, the measurement of the emotional state will allow us to make a real-time evaluation of how the users are feeling during the tasks, in this way the activity can be better conducted by adapting it so that it is as efficient as possible and takes us to good results.
Music will be studied as a motivating factor and for improving the emotional state when approaching rehabilitation therapies.
There will be 4 sessions during which measurements will be recorded.
1: measurement of this parameter when he or she is in an activity of daily life that is pleasurable. 2: measurement of this parameter when he or she is in an activity of daily life that is of discomfort. 3: Measurement of this parameter during the performance of rehabilitation activities. 4: Measurement of this parameter during rehabilitation activities accompanied with music according to the preferences.
CONTEXT AND MEASUREMENT FREQUENCIES
Four sessions will be held, divided into two parts:
PART 1: Selection of dependent variables. The aim of the first two sessions is to be able to count a reference level for physiological variables in activities that provoke pleasant and unpleasant emotions, so that they can be used as a reference in Part 2; the purpose is to try to avoid dependence on the EVEA tests since the subjects will not always be able to express their emotions. The EVEA test is used as a reinforcer for a possible automatic classifier.
Half of the participants will start with session 2 and then do session 1, while the rest will follow the reverse order.
PART 2. Effect of music on the dependent variables during the performance of rehabilitation exercises
The pleasant motivational music to be played during session 4 will be selected by each user according to his or her musical preferences, or, failing that, will be transmitted to us by his or her caregiver. The rehabilitation activity should be a light exercise for the user, such as pedaling, limb extension, or any other that is measurable through inertial units. The specific activity that each user will have to perform will be determined by the medical staff and/or physiotherapist of each center, as it will be limited by the movement capacity of each participant.
Although each session has a different theme, the structure of the sessions is similar. First, the sensors are placed on the volunteer; once it has been verified that the data are collected in an adequate manner, the data recording begins while the user is answering the EVEA test. This first part of the recording will be used as a baseline for the session, which should last at least two minutes. After that, the activity will start, which will not last more than 15 minutes; and to finish, a new EVEA test will be filled in, with identical restrictions to the first test. With these initial and final baselines, the differential of the measurements of each session can be detected, in addition to the analysis of the evolution of the subject during the activity.
For each user, the protocol should be completed in two weeks, during the first week sessions 1 and 2, and during the second week sessions 3 and 4.
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| Measure | Description | Time Frame |
|---|---|---|
| Average kinetic energy measurements (in joules) using inertial sensors | Four wearable devices in wrist, ankle, chest and head are used. All have inertial units. They provide information in the different contexts (scheduled sessions) about the energy expenditure that these entail. It should be studied whether this parameter is related to the emotional state. | Fifteen minutes. |
| Instantaneous Heart Rate (in seconds) | We used a wearable placed in the chest with Ag/AgCl electrodes for ECG, placed following the Einthoven's II lead positions. The position of R wave is determined using an appropriate algorithm and then time difference between two consecutive R waves is calculated, this time difference is used to calculed HR. We used 30s-length sliding windows with an overlap of 50%. The instantaneous HR is given by the average HR in such a window after removing the outliers. | Fifteen minutes. |
| The ratio between low frequency, (LF) and high frequency, (HF), (LF/HF) | We used a wearable placed in the chest with Ag/AgCl electrodes for Electrocardiogram (ECG) ,placed following the Einthoven's II lead positions. The ratio between low frequency , [0.04 - 0.15] Hz (LF) and high frequency, [0.15 - 4] Hz (HF) components of HRV, (LF/HF), shows the balance between the SNS (Sympathetic Nervous System) and the PNS (Parasympathetic Nervous System). | Fifteen minutes. |
| Temporal parameters of Heart Rate Variability (HRV) | We used a wearable placed in the chest with Ag/AgCl electrodes for Electrocardiogram (ECG) ,placed following the Einthoven's II lead positions. The HRV is especially interesting because it allows to assess the activity of the parasympathetic and sympathetic pathways of the ANS (Autonomic Nervous System). HVR can be measured using temporal parameters such as: SDNN Standard deviation of NN intervals; RMSSD Root mean square of successive differences between normal heartbeats; pNN50 Percentage of successive RR intervals that differ by more than 50 ms. |
| Measure | Description | Time Frame |
|---|---|---|
| International Classification of Functioning, Disability and Health (ICF) | The ICF is a "universal framework for the definition, measurement and policy formulations for health and disability", developed by the WHO and used in health-related sectors.This scale is used to measure several domains in adults: functional ability, cognitive and communication capacities and the quality of life related to health. It is graduated from 0 (no) to 4 (complete). |
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Inclusion Criteria:
1.People with a recognized disability, caused by a permanent illness or health situation.
Exclusion criteria are:
Present any health situation that is incompatible with the use of assistive technology designed and prototype in the project.
Have a very limited cognitive ability, which prevents you from following the instructions for the proper use of assistive technology.
Not having adequate human support.
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The population who took part in this study attend two different centers for people with special needs: Asociación Sevillana de Parálisis Cerebral (ASPACE) and Centro Específico de Educación Especial Mercedes Sanromá (CEEEMS).
ASPACE is a private organization catering mainly for adults with CP. The other center, CEEEMS, is a public specialist school that forms part of the educational network in Andalusia (Spain) and deals mainly with children and teenagers with motor dysfunctions (including CP).
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| Name | Affiliation | Role |
|---|---|---|
| Isabel M. Gomez-Gonzalez, Phd | University of Seville | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Isabel M. Gomez | Seville | Andalusia | 41012 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | R. Martinez; A. Salazar-Ramirez; A. Arruti; E. Irigoyen; J. I. Martin; J. Muguerza. A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis. 2019. IEEE Access PP(99):1-1 | ||
| Background | Can Y.S., Chalabianloo N., Ekiz D., Fernandez-Alvarez J., Repetto C., Riva G., Iles-Smith H., Ersoy C. Real-Life Stress Level Monitoring Using Smart Bands in the Light of Contextual Information. IEEE Sensors Journal. 2020. | ||
| 32033498 | Background | Rincon JA, Costa A, Novais P, Julian V, Carrascosa C. ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment. Sensors (Basel). 2020 Feb 5;20(3):852. doi: 10.3390/s20030852. | |
| Background | Correa, J.A.M.; Abadi, M.K.; Sebe, N.; Patras, I. Amigos: a dataset for affect, personality and mood research on individuals and groups. IEEE Trans. Affect. Comput. 2018. |
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| ID | Term |
|---|---|
| D002547 | Cerebral Palsy |
| ID | Term |
|---|---|
| D001925 | Brain Damage, Chronic |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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| Fifteen minutes. |
| Tonic Skin Conductance Level (SCL) | This signal is the background tonic of the Electrodermal Activity signal (EDA). It will be measured by dry electrodes that were placed on the hearten and hypothenar eminences of the dominant hand. | Fifteen minutes. |
| Parameters of Phasic Skin Conductance Response (SCR) | It will be measured by dry electrodes that were placed on the hearten and hypothenar eminences of the dominant hand This signal are constituted by the rapid phase components of the Electrodermal Activity signal (EDA). An SCR that cannot be attributed to a distinct stimulus is referred to as non-specific skin conductance response (NS-SCR). This category includes the spontaneous fluctuations in skin conductance that are our case because we measured the signal in periods without stimulus. | Fifteen minutes. |
| Fractal dimension of Electroencephalogram signal (EEG) | The EEG portrays the functioning of the brain. the recording of those signals will be done at a sampling rate of 125 Hz by OpenBCI. OpenBCI (https://openbci.com/) is a low-cost open-hardware device for the measure of EEG signals using 16 channels in positions FP1, FP2, F1, F2, F5, F6, Cz, C3, C4, T7, T8, Pz, P3, P4, O1, O2. The EEG signals are highly complex and dynamic in nature. Fractal dimension (FD) is emerging as a novel feature for computing its complexity. We will use the Higuchi's algorithm. | Fifteen minutes. |
| Spectral Entropy (SE) of EEG signal | The EEG portrays the functioning of the brain. the recording of those signals will be done at a sampling rate of 125 Hz by OpenBCI. SE can be used for computing EEG complexity. To do that, the power spectral density (PSD) must be obtained as a first step . After normalizing the PSD by the number of bins, which can be viewed as a probability density function conversion, the classical Shannon's entropy for information systems is then calculated. | Fifteen minutes. |
| EEG coherence | The interactions between neural systems, operating in each frequency band, are estimated by means of the EEG coherence. While neural synchronization influences EEG amplitude, the coherence between signals captured by one pair of electrodes refers to the consistence and stability of the signal amplitude and its phase. Two brain areas connected should show a signal delay in time domain that is measured as a phase shift in the frequency domain. | Fifteen minutes. |
| Activity of the regions in the brain | We will use Loreta. Loreta is a specific solution to the inverse problem, using algorithms that localize the cortical generators of the observed neuronal firing. | Fifteen minutes. |
| Through study completion, an average of 2 weeks. |
| Gross Motor Function Classification System (GMFCS) | The GMFCS is a five-level scale focused on truncal control and walking. The discrimination at each level of motor function is based on functional limitation and the use (or not) of assistive devices such as walkers, wheelchairs, etc. The scale goes from I: the ability to ambulate to V: dependent on AT for all mobility. | Through study completion, an average of 2 weeks. |
| Manual Ability Classification System (MACS) | The MACS scale assesses how children use both hands in situations of their daily life and whether they are independent or need some support. The opinion of people who know them and the age of the children are taken into account for this scale. The scale goes from I: Handles objects easily and successfully; V: Does not manipulate objects. Limited ability to perform simple actions. | Through study completion, an average of 2 weeks. |
| Communication Function Classification System (CFCS) | This scale is used to measure communication ability in children. The CFCS scale is a classification system for functional communication divided into five levels to identify performance in everyday communication. The scale goes from I: Efficient sender and receiver with known and unknown interlocutors; V: Sender and receiver rarely effective even with known interlocutors. | Through study completion, an average of 2 weeks. |
| KIDSCREEN Questionnaire | The KIDSCREEN instruments assess children's and adolescents' subjective health and well-being. They were developed as self-report measures applicable for healthy and chronically ill children and adolescents aged from 8 to 18 years. | Through study completion, an average of 2 weeks. |
| SCALE FOR MOOD ASSESSMENT (EVEA) | The EVEA was developed as an instrument "to measure transitory moods in studies using mood induction procedures", but it can be used whenever there is a need to measure transitory moods at any one time. The EVEA is composed of 16 items. Each item has an 11-point Likert scale (from 0 to 10), flanked by the words "not at all" (0) and "very much" (10), that presents, in its left margin, a short statement describing a mood. All 16 statements have the same structure; all of them begin with the expression "I feel" and end with an adjective describing a mood (e.g., "I feel sad", "I feel happy"). The EVEA tries to assess four moods; anxiety, anger-hostility, sadness-depression, and happiness. Each mood is measured by four items with different adjectives, and these four items define a subscale. All items of a given subscale are worded in the same direction. | Through study completion, an average of 2 weeks. |
| Background | Price E., Moore G., Galway L., Linden M. Towards mobile cognitive fatigue assessment as indicated by physical, social, environmental, and emotional factors. IEEE Access. 2019. |
| Background | Qureshi S., Hagelbäck J., Iqbal S.M.Z., Javaid H., Lindley C.A. Evaluation of classifiers for emotion detection while performing physical and visual tasks: Tower of Hanoi and IAPS. Intelligent Systems Conference 2018. |
| 31520963 | Background | Belmonte S, Montoya P, Gonzalez-Roldan AM, Riquelme I. Reduced brain processing of affective pictures in children with cerebral palsy. Res Dev Disabil. 2019 Nov;94:103457. doi: 10.1016/j.ridd.2019.103457. Epub 2019 Sep 11. |
| Background | Albiol-Pérez S., Cano S., Da Silva M.G., Gutierrez E.G., Collazos C.A., Lombano J.L., Estellés E., Ruiz M.A. A novel approach in virtual rehabilitation for children with cerebral palsy: Evaluation of an emotion detection system. Advances in Intelligent Systems and Computing. 2018. |
| Background | C. Rosales; L. Jácome; J. Carrión; C. Jaramillo; M. Palma. Computer vision for detection of body expressions of children with cerebral palsy.2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM). |
| Background | Kalansooriya P., Ganepola G.A.D,Thalagala T.S. Affective gaming in real-time emotion detection and Smart Computing music emotion recognition: Implementation approach with electroencephalogram. Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2020. |
| Background | Molina Cantero, Alberto Jesus, Gómez González, Isabel María, Merino Monge, Manuel, Castro García, Juan Antonio, Cabrera Cabrera, Rafael: Emotions detection based on a single-electrode EEG device. Comunicación en congreso. 4 ª International Conference on Physiological Computing Systems. - Madrid,. 2017 |
| 26737830 | Background | Merino M, Gomez I, Molina AJ. EEG feature variations under stress situations. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6700-3. doi: 10.1109/EMBC.2015.7319930. |
| Background | Merino Monge, Manuel, Gómez González, Isabel María, Castro García, Juan Antonio, Molina Cantero, Alberto Jesus, Quesada, Roylan: A Preliminary Study about the Music Influence on EEG and ECG Signals. Comunicación en congreso. 5th International Conference on Physiological Computing Systems. Sevilla. 2018 |
| Background | Castro García, Juan Antonio, Molina Cantero, Alberto Jesus, Merino Monge, Manuel, Gómez González, Isabel María: An Open-Source Hardware Acquisition Platform for Physiological Measurements. En: IEEE Sensors Journal. 2019. Vol. 19. 10.1109/Jsen.2019.2933917 |
| 38793103 | Derived | Gomez-Gonzalez IM, Castro-Garcia JA, Merino-Monge M, Sanchez-Anton G, Hamidi F, Mendoza-Sagrera A, Molina-Cantero AJ. Emotional State Measurement Trial (EMOPROEXE): A Protocol for Promoting Exercise in Adults and Children with Cerebral Palsy. J Pers Med. 2024 May 14;14(5):521. doi: 10.3390/jpm14050521. |