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Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to... more
Engagement in activities is of crucial importance for people with dementia. State of the art assessment techniques rely exclusively on behavior observation to measure engagement in dementia. These techniques are either too general to grasp how engagement is naturally expressed through behavior or too complex to be traced back to an overall engagement state. We carried out a longitudinal study to develop a coding system of engagement-related behavior that could tackle these issues and to create an evidence-based model of engagement to make meaning of such a coding system. Fourteen elderlies with mild to moderate dementia took part in the study. They were involved in two activities: a game-based cognitive stimulation and a robot-based free play. The coding system was developed with a mixed approach: ethographic and Laban-inspired. First, we developed two ethograms to describe the behavior of participants in the two activities in detail. Then, we used Laban Movement Analysis (LMA) to i...
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A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and... more
A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods). The goal of this study was to analyze the accuracy of this algorithm under real conditions of use. This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard. The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm...
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In this paper, we present a novel tool to measure engagement in people with dementia playing board games and interacting with a social robot, Pleo. We carried out two studies to reach a comprehensive inventory of behaviours accounting for... more
In this paper, we present a novel tool to measure engagement in people with dementia playing board games and interacting with a social robot, Pleo. We carried out two studies to reach a comprehensive inventory of behaviours accounting for engagement in dementia. The first one is an exploratory study aimed at modelling engagement in cognitive board games. The second one is a longitudinal study to investigate how people with dementia express engagement in cognitive games and in interactions with social robots. We adopted a technique coming from Ethology to mould behaviour, the ethogram. Ethogram is founded on low level behaviours, and allows hierarchical structuring. Herein, we present preliminary results consisting in the description of two ethograms and in their structuring obtained through thematic analysis. Such results show that an underlying structure of engagement exists across activities, and that different activities trigger different behavioural displays of engagement that a...
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Research Interests: Bioengineering, Education, Health Behavior, Frailty, Aging, and 15 moreEmpowerment, Coaching, Humans, Europe, Chronic Disease, Journal Article, Action Plan, Aged, Citizen Empowerment, Falls, European Continental Ancestry Group, Integrated Care, Cooperative Behavior, Frail elderly, and Accidental falls
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ABSTRACT Parkinson's disease (PD) is the second most common neuro‐ degenerative disorder. First appreciable symptoms in PD are those re‐ lated to an altered movement control. Current PD treatments temporally revert the symptoms,... more
ABSTRACT Parkinson's disease (PD) is the second most common neuro‐ degenerative disorder. First appreciable symptoms in PD are those re‐ lated to an altered movement control. Current PD treatments temporally revert the symptoms, but they do not prevent disease's progression. At the beginning of the treatment, the antiparkinsonian effect of the medi‐ cation is very evident and symptoms may completely disappear for hours; however, as disease progresses, motor fluctuations appear. Col‐ lecting precise information on the temporal course of fluctuations is es‐ sential for tailoring an optimal therapy in PD patients and is one of the main parameters in clinical trials. This paper presents an algorithm for wearable devices to automatically detect patient's motor fluctuations based on inertial sensors. The algorithm has been evaluated in 7 PD pa‐ tients at their homes without supervision and performing their usual ac‐ tivities. Results are a mean sensitivity of 99.9% and a mean specificity of 99.9%.
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In this article, we introduce the theory of archetype, which explains the connection between ancient myths and the human mind. Based on the assumption that archetypes are in the deepest level of human mind, we propose that archetypal... more
In this article, we introduce the theory of archetype, which explains the connection between ancient myths and the human mind. Based on the assumption that archetypes are in the deepest level of human mind, we propose that archetypal symbolism is a kind of knowledge that supports the cognitive process for creating subjective world-view towards the physical world we live in. According to archetypal symbolism, we conducted an empirical study to identify archetypal symbols in modern movies. A new collection of movie clips was developed to represent eight essential archetypes: anima, animus, mentor, mother, shadow, hero’s departure, hero’s initiation and hero’s return, which can be used in future studies on human emotion. In order to investigate the emotions towards these archetypal symbols, we provide suggestions from the psychological point of view. The present study demonstrates how to identify symbolic meanings in movies, and indicates a new direction for future studies in psychology.
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Posture transitions (PT) are important movements among the activities performed in daily life of older adults. Their analysis provides information related to the amount of activity performed by a patient over a day and, furthermore, they... more
Posture transitions (PT) are important movements among the activities performed in daily life of older adults. Their analysis provides information related to the amount of activity performed by a patient over a day and, furthermore, they are useful for assessing symptoms in some movement disorders such as Parkinson’s disease. Many research works have attempted to automatically identify PT relying on the use of machine learning algorithms and light and small accelerometers, since they might be embedded into wearable systems, being unobtrusive for the users. However, distinguishing PTs through a single sensor results in complex classifiers requiring high computational resources, since some PT (such as Stand-to-Sit and Sit-to-Stand PT) may provide very similar acceleration signals. In this paper, we propose a barometer sensor with the aim of complementing the information provided by accelerometers. In addition, a hierarchical algorithm is presented, which is based on Support Vector Machines to detect PT including falls and Lying-to-Stand PT through a single sensor device. Results in 14 users show that the use of a barometer sensor enables the hierarchical algorithm to distinguish Sit-to-Stand from Stand-to-Sit transitions, and Falls from Lying-to-Stand with accuracies over 99%.
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Freezing of Gait (FoG) is one of the most disturbing symptoms in Parkinson’s disease (PD). Current algorithms that detect this symptom depend on frequency features extracted from wearable systems. These algorithms have only been evaluated... more
Freezing of Gait (FoG) is one of the most disturbing symptoms in Parkinson’s disease (PD). Current algorithms that detect this symptom depend on frequency features extracted from wearable systems. These algorithms have only been evaluated under laboratory conditions and, in real life, they might present false positives, reducing the reliability of the algorithm. This paper presents the evaluation of 20 PD patients in their homes and the inclusion of a posture algorithm to contextualize FoG detection. This algorithm, in average, improves specificity a 5% while preserves the sensitivity. In some patients, specificity increases by 11.9% maintaining the sensitivity.
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Research Interests: Computer Science and Gait
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Parkinson's disease (PD) is a neurodegenerative disease that predominantly alters patients' motor performance. Reduced step length and inability of step are important symptoms associated with PD. Assessing patients' motor... more
Parkinson's disease (PD) is a neurodegenerative disease that predominantly alters patients' motor performance. Reduced step length and inability of step are important symptoms associated with PD. Assessing patients' motor state monitoring step length helps to detect periods in which patients suffer lack of medication effect. Evaluate the adaption of existing step length estimation methods based on accelerometer sensors to a new position on left lateral side of waist in 28 PD patients. In this paper, a user-friendly position, the lateral side of the waist, is selected to place a tri-axial accelerometer. A newly developed step detection algorithm - Sliding Window Averaging Technique (SWAT) is evaluated in detecting steps using signals from this location. The detected steps are then used to estimate step length using four proposed correction factors for Zijlstra's, Gonzalez's and Weinberg's methods that were originally developed for the signals from lower back. ...
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The rise of ubiquitous computing systems in our environment is engendering a strong need for novel approaches of human-computer interaction. Either for extending the existing range of possibilities and services available to people or for... more
The rise of ubiquitous computing systems in our environment is engendering a strong need for novel approaches of human-computer interaction. Either for extending the existing range of possibilities and services available to people or for providing assistance the ones with limited conditions. Human Activity Recognition (HAR) is playing a central role in this task by offering the input for the development of more interactive and cognitive environments. This has motivated the organization of the ESANN 2013 Special Session in Human Activity and Motion Disorder Recognition and the execution of a competition in HAR. Here, a compilation of the most recent proposals in the area are exposed accompanied by the results of the contest calling for innovative approaches to recognize activities of daily living (ADL) from a recently published data set.
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La deteccion de caidas mediante un sensor basado en microcontrolador y un sensor de aceleracion triaxial, se basa en detectar un patron en la senal medida por el acelerometro durante una caida
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In order to enhance the quality of life of people with mobility problems like Parkinson's disease or... more
In order to enhance the quality of life of people with mobility problems like Parkinson's disease or stroke patients, it is crucial to monitor and assess their daily life activities by characterizing basic movements like postural transitions, which is the main goal of this work. This paper presents a novel postural transition detection algorithm which is able to detect and identify Sit to Stand and Stand to Sit transitions with a Sensitivity of 88.2% and specificity of 98.6% by using a single sensor located at the user's waist. The algorithm has been tested with 31 healthy volunteers and an overall amount of 545 transitions. The proposed algorithm can be easily implemented in real-time system for on-line monitoring applications.
Research Interests: Physiology, Computer Science, Instrumentation, Human, Library and Information Studies, and 15 moreHumans, Transducers, Movement, Article, Acceleration, Posture, Actigraphy, Reproducibility, Reproducibility of Results, Ambulatory BP monitoring, Body Posture, Sensitivity and Specificity, Transducer, Ambulatory Monitoring, and Inertial Measurement Unit
Research Interests: Computer Science and Gait
Patients with severe idiopathic... more
Patients with severe idiopathic Parkinson's disease experience motor fluctuations, which are often difficult to control. Accurate mapping of such motor fluctuations could help improve patients' treatment. The objective of the study was to focus on developing and validating an automatic detector of motor fluctuations. The device is small, wearable, and detects the motor phase while the patients walk in their daily activities. Algorithms for detection of motor fluctuations were developed on the basis of experimental data from 20 patients who were asked to wear the detector while performing different daily life activities, both in controlled (laboratory) and noncontrolled environments. Patients with motor fluctuations completed the experimental protocol twice: (1) once in the ON, and (2) once in the OFF phase. The validity of the algorithms was tested on 15 different patients who were asked to wear the detector for several hours while performing daily activities in their habitual environments. In order to assess the validity of detector measurements, the results of the algorithms were compared with data collected by trained observers who were accompanying the patients all the time. The motor fluctuation detector showed a mean sensitivity of 0.96 (median 1; interquartile range, IQR, 0.93-1) and specificity of 0.94 (median 0.96; IQR, 0.90-1). ON/OFF motor fluctuations in Parkinson's patients can be detected with a single sensor, which can be worn in everyday life.
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Parkinson's Disease (PD) is a neurodegenerative disease that alters the... more
Parkinson's Disease (PD) is a neurodegenerative disease that alters the patients' motor performance. Patients suffer many motor symptoms: bradykinesia, dyskinesia and freezing of gait, among others. Furthermore, patients alternate between periods in which they are able to move smoothly for some hours (ON state), and periods with motor complications (OFF state). An accurate report of PD motor states and symptoms will enable doctors to personalize medication intake and, therefore, improve response to treatment. Additionally, real-time reporting could allow an automatic management of PD by means of an automatic control of drug-administration pump doses. Such a system must be able to provide accurate information without disturbing the patients' daily life activities. This paper presents the results of the MoMoPa study classifying motor states and dyskinesia from 20 PD patients by using a belt-worn single tri-axial accelerometer. The algorithms obtained will be validated in a further study with 15 PD patients and will be enhanced in the REMPARK project.
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ABSTRACT Analysis of human movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease (PD) or stroke patients, it is crucial... more
ABSTRACT Analysis of human movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease (PD) or stroke patients, it is crucial to monitor their daily life activities. The main goal of this work is to characterize basic activities and their transitions using a single sensor located at the waist. This paper presents a novel postural detection algorithm which is able to detect and identify 6 different postural transitions, sit to stand, stand to sit, bending up/down and lying to sit and sit to lying transitions with a sensitivity of 86.5% and specificity of 95%. The algorithm has been tested on 31 healthy volunteers and 8 PD patients who performed a total of 545 and 176 transitions respectively. The proposed algorithm is suitable to be implemented in real-time systems for on-line monitoring applications.
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ABSTRACT Parkinson’s Disease (PD) is a neurodegenerative disease that predominantly alter patients’ motor performance and compromises the speed, the automaticity and fluidity of natural movements. The patients fluctuate between periods in... more
ABSTRACT Parkinson’s Disease (PD) is a neurodegenerative disease that predominantly alter patients’ motor performance and compromises the speed, the automaticity and fluidity of natural movements. The patients fluctuate between periods in which they can move almost normally for some hours (ON state) and periods with motor disorders (OFF state). Gait properties are affected by the motor state of a patient: reduced stride length, reduced gait speed, increased stride width etc. The ability to assess the motor states (ON/OFF) on a continuous basis for long time without disturbing the patients’ daily life activities is an important component of PD management. An accurate report of motor states could allow clinics to adjust the medication regimen to avoid OFF periods. The real-time monitoring will also allow an online treatment by combining, for instance, with automatic drug-administration pump doses. Many studies have attempted to extract gait properties through a belt-worn single tri-axial accelerometer. In this paper, a user friendly position is proposed to place the accelerometer and three step detection methods and three step length estimators are compared considering the proposed sensor placement in signals obtained from healthy volunteers and PD patients. Adaptation methods to these step length estimators are also proposed and compared. The comparison shows that the adapted estimators improve the performance with the new proposed step detection method and reduce errors in respect of the original methods.
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In research on emotion, presenting affective stimuli has been believed to be an effective and reliable technique for emotion elicitation. Instead of collecting stimuli for pre-defined emotions, we propose to develop stimuli based on their... more
In research on emotion, presenting affective stimuli has been believed to be an effective and reliable technique for emotion elicitation. Instead of collecting stimuli for pre-defined emotions, we propose to develop stimuli based on their symbolic meanings. We adopted archetypal symbolism as a standard to edit eight movie clips of archetypes as a new set of affective stimuli. These stimuli were used in an experiment for emotion elicitation. Participants' emotional responses toward these stimuli of archetypes were measured by the self-report technique and the physiological measurement. The results of linear discriminant analysis show that physiological measurement is more robust than the self-report techniques in recognizing emotions toward stimuli of archetypes. However, it is still unclear which technique reflects the ground truth of human emotion. We discuss alternative implications of these results, and provide more research questions for future studies on emotion recognition...
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Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of... more
Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson's disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 × 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients' symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 × 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykine...