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  • Centre d'Estudis Tecnològics per a l'atenció a la Dependència i vida autònoma

     Cabestany Moncusi, Joan; Moreno Arostegui, Juan Manuel; Madrenas Boadas, Jordi; Cosp Vilella, Jordi; Llanas Parra, Francesc Xavier; Sama Monsonis, Albert; Perez Lopez, Carlos; Rodriguez Martin, Daniel Manuel; Reyes Ortiz, Jorge Luis; Sayeed, Taufique; Takac, Boris; Khan, Rafiullah; Huang-Ming, Chang; Bano, Sophia; Català Mallofré, Andreu
    Competitive project

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  • prórroga del TIN2010-20966-C02-02 - APRENDIZAJE AUTOMATICO DE CAPACIDADES SENSORIALES MEDIANTE MAQUINAS DE SOPORTE VECTORIAL

     Sanchez Soler, Monica; Ruiz Vegas, Francisco Javier; Prats Duaygues, Francesc; Aguado Chao, Juan Carlos; Sama Monsonis, Albert
    Competitive project

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  • Comparative and adaptation of step detection and step length estimators to a lateral belt worn accelerometer

     Sayeed, Taufique; Sama Monsonis, Albert; Català Mallofré, Andreu; Cabestany Moncusi, Joan
    IEEE International Conference on e-Health Networking, Applications and Services
    p. 105-109
    Presentation's date: 2013-10-10
    Presentation of work at congresses

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    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.

    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.

  • Human movement analysis by means of accelerometers: Aplication to human gait and motor symptoms of Parkinson's Disease  Open access

     Sama Monsonis, Albert
    Universitat Politècnica de Catalunya
    Theses

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    La tesis que este documento recoge es una aportación al análisis del movimiento humano a partir de las señalescapturadas por acelerómetros. Estos sensores permiten convertir la aceleración producida por algunas partes del cuerpoen señales eléctricas susceptibles de un análisis posterior. El progresivo perfeccionamiento y miniaturización de losacelerómetros ha permitido construir sensores poco invasivos que pueden ser usados de forma ambulatoria para registrarlos movimientos realizados en las actividades de la vida diaria del sujeto.La tesis se focaliza en dos ámbitos. El primero, fundamentalmente clínico, se ha centrado en el análisis del movimiento enenfermos de la Enfermedad de Parkinson (EP). El segundo ámbito, más general, ha consistido en relacionar las señalesacelerométricas con características de la marcha. Con este fin, se han desarrollado métodos para la obtención de lavelocidad de la marcha y la longitud del paso, así como para la identificación de personas. En ambos ámbitos se haempleado un único acelerómetro situado en un lado de la cintura.La EP es una enfermedad neurodegenerativa que produce primordialmente trastornos del movimiento en los pacientes quela sufren. Los principales sóntomas motores de la enfermedad son 1) los temblores, 2) la lentitud de movimientos obradicinesia, 3) la congelación de la marcha (freezing of gait FoG) y 4) los movimientos involuntarios o discinesias. Los tresprimeros síntomas aparecen cuando la medicación no ha alcanzado un efecto terapeútico efectivo. Estos periodos seconocen habitualmente como período o estado motor OFF. Los periodos en los cuales la medicación hace efecto y lospacientes presentan una movilidad normal (o casi normal) son los periodos o estados motores ON. Las discinesiasaparecen principalmente cuando el nivel de medicación en sangre es excesivo. Tanto las discinesias como los estadosOFF son consecuencia de un defecto en la administración de la medicación. Un dispositivo no invasivo que detecte yregistre las discinesias y ambos periodos ON y OFF supone una importante herramienta que permite al médico prescribircon mayor precisión la dosis de medicamento adecuada a su paciente.El trabajo realizado en esta tesis en el ámbito de la EP ha consistido en el desarrollo de algoritmos de detección dediscinesias y periodos OFF. Estos algoritmos han sido adaptados para proporcionar una detección en tiempo real, de formaque se han empleado ya en un estudio piloto en los que el ajuste de medicación suministrada por una bomba de infusiónsubcutánea se realiza de forma automática en función de la presencia de discinesias y el estado motor del paciente.La experiencia ganada en el tratamiento de la señal acelerométrica proveniente de enfermos de Parkinson ha permitidocontribuir en el campo del análisis de la marcha y realizar una aportación que relaciona varios parámetros de la misma conla señal que suministra un único acelerómetro situado en la cintura. No solo la EP puede beneficiarse de este estudio, sinotambién otras enfermedades como la Diabetes o algunas enfermedades ortopédicas y traumatológicas puedenaprovecharse de sus resultados.Por útimo, usando algunas de las ténicas de los estudios anteriores, se ha realizado una importante contribución en elámbito de la identificación biométrica de personas. Se ha puesto de manifiesto que la señal proveniente de un únicoacelerómetro situado en la cintura no solo permite obtener algunas de las características de la marcha sino tambiénpermite identificar a la persona a través del patrón de su marcha. La principal contribución teórica de esta tesis ha sido eldesarrollo de técnicas basadas en la reconstrucción de atractores. Se ha evidenciado que un número muy reducido decaracterísticas procedentes del atractor reconstruido a partir de una serie temporal de medidas de aceleración permite laextracción de los parámetros de la marcha y la identificación de personas.

    This thesis presents the original contributions of the author on the field of human movement analysis from signals captured by accelerometers. These sensors are capable of converting acceleration from some body parts into electric signals for further analysis. The progressive refinement and miniaturization of accelerometers has allowed the development of minimally invasive devices that can be used to ambulatory monitor human movements during daily live activities. The study's contributions mainly fall under two heads: first, the analysis of movement in Parkinson's disease (PD); and, second, the relationship between accelerometer signals and characteristics of gait. To this end, new methods for obtaining speed and length of strides and, also, for identifying people have been developed. In all these studies, a single sensor fixed to the patient's waist has been used. PD is a neurodegenerative disease characterized by movement alterations. The main motor symptoms of PD are 1) tremor, 2) bradykinesia or slowness of movements, 3) freezing of gait and 4) dyskinesia or abnormal involuntary movements. The first three symptoms primarily occur when the medication has not yet reached an effective therapeutic effect. These periods are commonly known as OFF periods or OFF motor state. On the other hand, periods when the patient is suitably responding to the medication are known as ON periods or ON motor state. Dyskinesias mainly appear when the medication blood level is excessive. Both dyskinesias and OFF motor states are caused by a defect in the medication administration. In this sense, a wearable device capable of detecting and recording dyskinesias and OFF periods represents an important tool that enables clinics to more accurately prescribe the medication regimen of a patient. The work done in the field of PD consisted in developing algorithms able to detect dyskinesias and both ON and OFF periods. These algorithms have been adapted to provide real-time detection, which enabled their employment in a pilot study. This clinical study has tested, for the first time, the automatic adjustment of medication performed by means of a subcutaneous infusion pump according to the dyskinesias appearance and motor state of PD patients. The experience gained in the treatment of accelerometric signals from PD has led to contribute in the field of gait analysis. First, new methods for obtaining speed and length of strides from a single sensor fixed to the patient's waist have been obtained. Not only the PD can benefit from this study, but other diseases such as diabetes or some orthopedotraumatological diseases can also benefit from its results. Finally, using some of the techniques of the previous studies, another important contribution has been made in the field of biometric person identification. The work presented shows how the signal obtained from a single accelerometer located at the waist not only enables the extraction of some gait characteristics but also permits the identification of a person through its gait pattern. The main theoretical contribution of this thesis has been the development of techniques based on the reconstruction of attractors. It has been shown that the usage of only a small number of features that characterize the reconstructed attractor obtained from a time series of acceleration measurements makes possible the extraction of important parameters of gait and the person identification.

    La tesis que este documento recoge es una aportaci on al an alisis del movimiento humano a partir de las señales capturadas por aceler ometros. Estos sensores permiten convertir la aceleraci on producida por algunas partes del cuerpo en señales el ectricas susceptibles de un an alisis posterior. El progresivo perfeccionamiento y miniaturización de los aceler ometros ha permitido construir sensores poco invasivos y que pueden ser usados de forma ambulatoria para registrar los movimientos realizados en las actividades de la vida diaria del sujeto. La tesis se focaliza en dos ambitos. El primero, fundamentalmente cl inico, se ha centrado en el an alisis del movimiento en enfermos de la Enfermedad de Parkinson (EP). El segundo ambito, m as general, ha consistido en relacionar las señales acelerom etricas con caracter sticas de la marcha. Con este fin, se han desarrollado m etodos para la obtenci on de la velocidad de la marcha y la longitud del paso, as í como para la identificaci on de personas. En ambos ambitos se ha empleado un unico aceler ometro situado en un lado de la cintura. La EP es una enfermedad neurodegenerativa que produce primordialmente trastornos del movimiento en los pacientes que la sufren. Los principales sí ntomas motores de la enfermedad son 1) los temblores, 2) la lentitud de movimientos o bradicinesia, 3) la congelaci on de la marcha (freezing of gait FoG) y 4) los movimientos involuntarios o discinesias. Los tres primeros s ntomas aparecen cuando la medicaci on no ha alcanzado un efecto terape utico efectivo. Estos periodos se conocen habitualmente como per odo o estado motor OFF. Los periodos en los cuales la medicaci on hace efecto y los pacientes presentan una movilidad normal (o casi normal) son los periodos o estados motores ON. Las discinesias aparecen principalmente cuando el nivel de medicaci on en sangre es excesivo. Tanto las discinesias como los estados OFF son consecuencia de un defecto en la administraci on de la medicaci on. Un dispositivo no invasivo que detecte y registre las discinesias y ambos periodos ON y OFF supone una importante herramienta que permite al m edico prescribir con mayor precisi on la dosis de medicamento adecuada a su paciente. El trabajo realizado en esta tesis en el ambito de la EP ha consistido en el desarrollo de algoritmos de detecci on de discinesias y periodos OFF. Estos algoritmos han sido adaptados para proporcionar una detecci on en tiempo real, de forma que se han empleado ya en un estudio piloto en los que el ajuste de medicaci on suministrada por una bomba de infusi on subcut anea se realiza de forma autom atica en funci on de la presencia de discinesias y el estado motor del paciente. La experiencia ganada en el tratamiento de la señal acelerom etrica proveniente de enfermos de Parkinson ha permitido contribuir en el campo del an alisis de la marcha y realizar una aportaci on que relaciona varios par ametros de la misma con la señal que suministra un unico aceler ometro situado en la cintura. No solo la EP puede bene ciarse de este estudio, sino tambi en otras enfermedades como la Diabetes o algunas enfermedades ortop edicas y traumatol ogicas pueden aprovecharse de sus resultados. Por ultimo, usando algunas de las t ecnicas de los estudios anteriores, se ha realizado una importante contribuci on en el ambito de la identi caci on biom etrica de personas. Se ha puesto de mani esto que la señal proveniente de un unico aceler ometro situado en la cintura no solo permite obtener algunas de las caracter sticas de la marcha sino tambi en permite identi car a la persona a trav es del patr on de su marcha. La principal contribuci on te orica de esta tesis ha sido el desarrollo de t ecnicas basadas en la reconstrucci on de atractores. Se ha evidenciado que un n umero muy reducido de caracter sticas procedentes del atractor reconstruido a partir de una serie temporal de medidas de aceleraci on permite la extracci on de los par ametros de la marcha y la identi caci on de personas

  • Comparison and adaptation of step length and gait speed estimators from single belt worn accelerometer positioned on lateral side of the body

     Sayeed, Taufique; Sama Monsonis, Albert; Català Mallofré, Andreu; Cabestany Moncusi, Joan
    IEEE International Symposium on Intelligent Signal Processing
    p. 14-20
    Presentation's date: 2013-09-17
    Presentation of work at congresses

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    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 drugadministration 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 six step length estimators are compared considering the proposed sensor placement in a preliminary database of healthy volunteers. Adaptation methods to some of these estimators are also proposed and compared. The comparison shows that the adapted estimators improve the performance and reduce errors in respect of the original methods applied in the new sensor location.

    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 drugadministration 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 six step length estimators are compared considering the proposed sensor placement in a preliminary database of healthy volunteers. Adaptation methods to some of these estimators are also proposed and compared. The comparison shows that the adapted estimators improve the performance and reduce errors in respect of the original methods applied in the new sensor location.

  • Identification of postural transitions using a waist-located inertial sensor

     Rodriguez Martin, Daniel Manuel; Sama Monsonis, Albert; Perez Lopez, Carlos; Català Mallofré, Andreu; Cabestany Moncusi, Joan; Rodríguez Molinero, Alejandro
    International Work-Conference on Artificial Neural Networks
    p. 142-149
    DOI: 10.1007/978-3-642-38682-4
    Presentation's date: 2013-06-13
    Presentation of work at congresses

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    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.

    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.

  • REMPARK: when AI and technology meet parkinson disease assessment

     Cabestany Moncusi, Joan; Perez Lopez, Carlos; Sama Monsonis, Albert; Moreno Arostegui, Juan Manuel; Bayes, Ángels; Rodríguez Molinero, Alejandro
    International Conference Mixed Design of Integrated Circuits and Systems
    p. 562-567
    Presentation's date: 2013-06
    Presentation of work at congresses

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    REMPARK project objective is to develop a personal health system with closed loop detection, response and action capabilities for the assessment and possible management of Parkinson's Disease (PD) patients. The project is developing a wearable monitoring system able to identify in real time the motor status of the PD patients and evaluating ON/OFF/Dyskinesia status with a very high sensitivity and specificity degree (>80%) in operation during ambulatory conditions. Identification of the motor status is based on the knowledge included in a large database obtained with the collaboration of a number of volunteer PD patients, according a specific defined protocol in ambulatory conditions. Artificial Intelligence (AI) methods are applied to the database information for the automatic detection of motor symptoms.

  • FATE: one step towards an automatic aging people fall detection service

     Cabestany Moncusi, Joan; Moreno Arostegui, Juan Manuel; Perez Lopez, Carlos; Sama Monsonis, Albert; Català Mallofré, Andreu; Rodríguez Molinero, Alejandro; Arnal, Marc
    International Conference Mixed Design of Integrated Circuits and Systems
    p. 545-552
    Presentation's date: 2013-06
    Presentation of work at congresses

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    FATE is a project funded by the European Union under the program CIP/ICT-PSP with the main objective of organizing a big pilot on the automatic falls detection in aging people living at home. Automatic detection of falls is done in indoors and outdoors conditions, and in both cases the detection generates an alarm sent to a call center. The detection system is designed around a sensor sub-system based on accelerometers and gyroscopes able to detect falls with a high reliability. The complete system is based on a communications layer based in ZigBee and Bluetooth protocols. The gateway for sending the alarm to the call center is a mobile phone. Pilots are organized in three different countries (Spain, Italy and Ireland) where different models of health service and implemented call centers are available. Pilots duration will be one year, involving 175 users and one of the main final objectives is to gain experience with the integration of an automatic fall detection service in an already care/health existing service.

  • Access to the full text
    A heterogeneous database for movement knowledge extraction in Parkinson's disease  Open access

     Sama Monsonis, Albert; Perez Lopez, Carlos; Rodriguez Martin, Daniel Manuel; Cabestany Moncusi, Joan; Moreno Arostegui, Juan Manuel; Rodríguez Molinero, Alejandro
    European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
    p. 413-418
    Presentation's date: 2013-04-26
    Presentation of work at congresses

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    This paper presents the design and methodology used to create a heterogeneous database for knowledge movement extraction in Parkinson's Disease. This database is being constructed as part of REM- PARK project and is composed of movement measurements acquired from inertial sensors, standard medical scales as Unied Parkinson's Disease Rating Scale, and other information obtained from 90 Parkinson's Disease patients. The signals obtained will be used to create movement disorder detection algorithms using supervised learning techniques. The different sources of information and the need of labelled data pose many challenges which the methodology described in this paper addresses. Some preliminary data obtained are presented.

    This paper presents the design and methodology used to create a heterogeneous database for knowledge movement extraction in Parkinson's Disease. This database is being constructed as part of REM- PARK project and is composed of movement measurements acquired from inertial sensors, standard medical scales as Uni ed Parkinson's Disease Rating Scale, and other information obtained from 90 Parkinson's Disease patients. The signals obtained will be used to create movement disorder detection algorithms using supervised learning techniques. The different sources of information and the need of labelled data pose many challenges which the methodology described in this paper addresses. Some preliminary data obtained are presented.

  • Active learning of actions based on support vector machines

     Ruiz Vegas, Francisco Javier; Sama Monsonis, Albert; Agell Jané, Núria
    International Conference of the Catalan Association for Artificial Intelligence
    p. 37-46
    DOI: 10.3233/978-1-61499-139-7-37
    Presentation's date: 2012-10-24
    Presentation of work at congresses

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  • Dyskinesia and motor state detection in Parkinson's disease patients with a single movement sensor

     Sama Monsonis, Albert; Perez Lopez, Carlos; Rodriguez Martin, Daniel Manuel; Romagosa Cabús, Jaume; Català Mallofré, Andreu; Cabestany Moncusi, Joan; Pérez Martínez, David Andrés; Rodríguez Molinero, A.
    IEEE Engineering in Medicine and Biology Society
    p. 1194-1197
    DOI: 10.1109/EMBC.2012.6346150
    Presentation's date: 2012-08
    Presentation of work at congresses

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    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.

  • Granular singular spectrum analysis for gait recognition

     Sama Monsonis, Albert; Ruiz Vegas, Francisco Javier
    International Workshop on Qualitative Reasoning
    Presentation's date: 2012-07-19
    Presentation of work at congresses

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    AoL: Action Learning: A methodology to capture expertise in adjustment tasks  Open access

     Ruiz Vegas, Francisco Javier; Sama Monsonis, Albert; Raya Giner, Cristobal; Agell Jané, Núria
    Jornadas de ARCA
    p. 95-99
    Presentation's date: 2012-06-26
    Presentation of work at congresses

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    It is well known that some people can perform a task with greater precision and accuracy than others: they are experts. In the past, experts were interviewed to find out why they have this expertise, but this was not always completely effective because often experts "don't know what they know". In this paper we propose a model of the process of making decisions performed by experts in the final adjustment of products task. Based on this model, we also propose a system based on a machine learning module that facilitates the capture of these expert skills. We give an example to illustrate the process proposed.

  • Fall Detector for the Elder

     Català Mallofré, Andreu; Moreno Arostegui, Juan Manuel; Sama Monsonis, Albert; Perez Lopez, Carlos; Cortes Garcia, Claudio Ulises; Martinez Velasco, Antonio Benito; Romagosa Cabús, Jaume; Rodriguez Martin, Daniel Manuel; Cabestany Moncusi, Joan
    Competitive project

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  • Personal Health Device for the Remote and Autonomous Management of

     Català Mallofré, Andreu; Moreno Arostegui, Juan Manuel; Perez Lopez, Carlos; Sama Monsonis, Albert; Sayeed, Taufique; Rodriguez Martin, Daniel Manuel; Cabestany Moncusi, Joan
    Competitive project

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    Gait recognition by using spectrum analysis on state space reconstruction  Open access

     Sama Monsonis, Albert; Ruiz Vegas, Francisco Javier; Perez Lopez, Carlos; Català Mallofré, Andreu
    Congrés Internacional de l¿Associació Catalana d¿Intel·ligència Artificial
    p. 228-236
    Presentation's date: 2011-10-28
    Presentation of work at congresses

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    This paper describes a method for identifying a person while walking by means of a triaxial accelerometer attached to the waist. Human gait is considered as a dynamical system whose attractor is reconstructed by time delay vectors. A Spectral Analysis on the state space reconstruction is used to characterize the attractor. The method is compared to other common methods used in gait recognition tasks through a preliminary test.

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    An interval technical indicator for financial time series forecasting  Open access

     Ruiz Vegas, Francisco Javier; Sama Monsonis, Albert; Sanchez, German; Sanabria, José A.; Agell Jane, Nuria
    International Workshop on Qualitative Reasoning
    p. 60-65
    Presentation's date: 2011-07-18
    Presentation of work at congresses

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    In this work we compare the performance of some standard technical indicators with an interval technical indicator, the moving interval (MI), for time series forecasting. MI has the advantage of taking into account the variability of data in the range considered and not only the average, like standard indicators do. However, the use of intervals as input variables require the use of regression methods able to handle with non Euclidean structures. The kernel approach is employed to this end. A recently introduced interval kernel is applied together with the moving interval indicator. The conclusion is that this indicator outperforms the forecasting performance of standard indicators.

  • Gait identification by using spectrum analysis on state space reconstruction

     Ruiz Vegas, Francisco Javier; Sama Monsonis, Albert; Perez Lopez, Carlos; Català Mallofré, Andreu
    International Work-Conference on Artificial Neural Networks
    p. 597-604
    DOI: 10.1007/978-3-642-21498-1_75
    Presentation's date: 2011-06-10
    Presentation of work at congresses

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    This paper describes a method for identifying a person while walking by means of a triaxial accelerometer attached to the waist. Human gait is considered as a dynamical system whose attractor is reconstructed by time delay vectors. A Spectral Analysis on the state space reconstruction is used to characterize the attractor. Parameters involved in the reconstruction and characterization process are evaluated to examine the effect in gait identification. The method is tested in five volunteers, obtaining an overall accuracy of 92%.

    Postprint (author’s final draft)

  • Complete Ambient Assisted Living Experiment-Market Validation (CAALYX-MV)

     Català Mallofré, Andreu; Cabestany Moncusi, Joan; Sama Monsonis, Albert; Llanas Parra, Francesc Xavier; Diaz Boladeras, Marta
    Competitive project

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  • CIP-ICT-PSP-2010-4, 250577 CAALIX-MW

     Angulo Bahón, Cecilio; Cabestany Moncusi, Joan; Llanas Parra, Francesc Xavier; Sama Monsonis, Albert; Perez Lopez, Carlos; Diaz Boladeras, Marta; Català Mallofré, Andreu
    Competitive project

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  • APRENDIZAJE AUTOMATICO DE CAPACIDADES SENSORIALES MEDIANTE MAQUINAS DE SOPORTE VECTORIAL

     Prats Duaygues, Francesc; Sanchez Soler, Monica; Rosello Sauri, Llorenç; Aguado Chao, Juan Carlos; Sama Monsonis, Albert; Ruiz Vegas, Francisco Javier
    Competitive project

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  • Method and device for detecting the On and Off states of a Parkinson patient

     Rodríguez Molinero, A.; Cabestany Moncusi, Joan; Català Mallofré, Andreu; Sama Monsonis, Albert; Gálvez Barrón, Cesar Pavel; Romagosa Cabús, Jaume; Pérez Martínez, David Andrés; Angulo Bahón, Cecilio; Perez Lopez, Carlos
    Date of request: 2010-08-01
    Invention patent

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  • Time series analysis of inertial-body signals for the extraction of dynamic properties from human gait

     Sama Monsonis, Albert; Pardo Ayala, Diego Esteban; Cabestany Moncusi, Joan; Rodríguez Molinero, A.
    IEEE World Congress on Computational Intelligence
    p. 3994-3998
    Presentation's date: 2010-07-19
    Presentation of work at congresses

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    Interval-valued feature selection  Open access

     Sama Monsonis, Albert; Ruiz Vegas, Francisco Javier; Català Mallofré, Andreu; Angulo Bahón, Cecilio
    Jornadas de ARCA
    p. 41-46
    Presentation's date: 2010-06-24
    Presentation of work at congresses

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    In this paper we introduce the use of interval variables in classification problems of time series signals. By introducing the concept of interval kernel as a similarity measure among intervals, modifications for some well-known feature selection methods are developed in order to apply these methods to select the most relevant interval variables. A comparison against standard point attributes feature selection (Relief and FSDD) is made for purposes of validation .

  • HOME-BASED EMPOWERED LIVING FOR PARKINSON'S DISEASENS PATIENTS

     Angulo Bahón, Cecilio; Sama Monsonis, Albert; Català Mallofré, Andreu; Pardo Ayala, Diego Esteban; Raya Giner, Cristobal; Perez Lopez, Carlos; Romagosa Cabús, Jaume; Rodriguez Martin, Daniel Manuel; Cabestany Moncusi, Joan
    Competitive project

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  • Gait identification by means of box approximation geometry of reconstructed attractors in latent space

     Sama Monsonis, Albert; Ruiz Vegas, Francisco Javier; Agell Jané, Núria; Perez Lopez, Carlos; Català Mallofré, Andreu; Cabestany Moncusi, Joan
    Neurocomputing
    Vol. 121, num. 9, p. 79-88
    DOI: 10.1016/j.neucom.2012.12.042
    Date of publication: 2013-02
    Journal article

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    This paper presents a novel gait recognition method which uses the signals measured by a single inertial sensor located on the waist. This method considers human gait as a dynamical system and employs a few singular values obtained by means of Singular Spectrum Analysis applied to scalar measurements from the inertial sensor. Singular values can be interpreted as the approximate edge length of the bounding box wrapping the attractor in the latent space. Effects of different parameters on the gait recognition performance using patterns from 20 different subjects are analysed.

  • SVM-based posture identification with a single waist-located triaxial accelerometer

     Rodriguez Martin, Daniel Manuel; Sama Monsonis, Albert; Perez Lopez, Carlos; Català Mallofré, Andreu; Cabestany Moncusi, Joan; Rodríguez Molinero, Alejandro
    Expert systems with applications
    Vol. 40, num. 18, p. 7203-7211
    DOI: 10.1016/j.eswa.2013.07.028
    Date of publication: 2013-12
    Journal article

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    Analysis of human body 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 o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson¿s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications.

    Analysis of human body 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 o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson’s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications.

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    A wearable inertial measurement unit for long-term monitoring in the dependency care area  Open access

     Rodriguez Martin, Daniel Manuel; Perez Lopez, Carlos; Sama Monsonis, Albert; Cabestany Moncusi, Joan; Català Mallofré, Andreu
    Sensors
    Vol. 13, num. 10, p. 14079-14104
    DOI: 10.3390/s131014079
    Date of publication: 2013-10-18
    Journal article

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    Human movement analysis is a field of wide interest since it enables the assessment of a large variety of variables related to quality of life. Human movement can be accurately evaluated through Inertial Measurement Units (IMU), which are wearable and comfortable devices with long battery life. The IMU's movement signals might be, on the one hand, stored in a digital support, in which an analysis is performed a posteriori. On the other hand, the signal analysis might take place in the same IMU at the same time as the signal acquisition through online classifiers. The new sensor system presented in this paper is designed for both collecting movement signals and analyzing them in real-time. This system is a flexible platform useful for collecting data via a triaxial accelerometer, a gyroscope and a magnetometer, with the possibility to incorporate other information sources in real-time. A SD card can store all inertial data and a Bluetooth module is able to send information to other external devices and receive data from other sources. The system presented is being used in the real-time detection and analysis of Parkinson's disease symptoms, in gait analysis, and in a fall detection system.

    Human movement analysis is a field of wide interest since it enables the assessment of a large variety of variables related to quality of life. Human movement can be accurately evaluated through Inertial Measurement Units (IMU), which are wearable and comfortable devices with long battery life. The IMU’s movement signals might be, on the one hand, stored in a digital support, in which an analysis is performed a posteriori. On the other hand, the signal analysis might take place in the same IMU at the same time as the signal acquisition through online classifiers. The new sensor system presented in this paper is designed for both collecting movement signals and analyzing them in real-time. This system is a flexible platform useful for collecting data via a triaxial accelerometer, a gyroscope and a magnetometer, with the possibility to incorporate other information sources in real-time. A μSD card can store all inertial data and a Bluetooth module is able to send information to other external devices and receive data from other sources. The system presented is being used in the real-time detection and analysis of Parkinson’s disease symptoms, in gait analysis, and in a fall detection system

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    Analyzing human gait and posture by combining feature selection and kernel methods  Open access

     Sama Monsonis, Albert; Angulo Bahón, Cecilio; Pardo Ayala, Diego Esteban; Català Mallofré, Andreu; Cabestany Moncusi, Joan
    Neurocomputing
    Vol. 74, num. 16, p. 2665-2674
    DOI: 10.1016/j.neucom.2011.03.028
    Date of publication: 2011-09
    Journal article

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    This paper evaluates a set of computational algorithms for the automatic estimation of human postures and gait properties from signals provided by an inertial body sensor. The use of a single sensor device imposes limitations for the automatic estimation of relevant properties, like step length and gait velocity, as well as for the detection of standard postures like sitting or standing. Moreover, the exact location and orientation of the sensor is also a common restriction that is relaxed in this study. Based on accelerations provided by a sensor, known as the `9 2', three approaches are presented extracting kinematic information from the user motion and posture. Firstly, a two-phases procedure implementing feature extraction and Support Vector Machine based classi cation for daily living activity monitoring is presented. Secondly, Support Vector Regression is applied on heuristically extracted features for the automatic computation of spatiotemporal properties during gait. Finally, sensor information is interpreted as an observation of a particular trajectory of the human gait dynamical system, from which a reconstruction space is obtained, and then transformed using standard principal components analysis, nally Support Vector Regression is used for prediction. Daily living Activities are detected and spatiotemporal parameters of human gait are estimated using methods sharing a common structure based on feature extraction and kernel methods. The approaches presented are susceptible to be used for medical purposes.

  • Identification of sit-to-stand and stand-to-sit transitions using a single inertial sensor

     Rodriguez Martin, Daniel Manuel; Sama Monsonis, Albert; Perez Lopez, Carlos; Català Mallofré, Andreu
    Studies in health technology and informatics
    Vol. 177, p. 113-117
    DOI: 10.3233/978-1-61499-069-7-113
    Date of publication: 2012
    Journal article

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    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.

    In order to enhance the people’s quality of life with mobility problems like Parkinson’s disease or stroke patients 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 into 31 healthy volunteers and an overall amount of 545 transitions. The proposed algorithm can be implemented easily in real-time system for on-line monitoring applications.

    Postprint (author’s final draft)