Time measurement characterization of stand-to-sit and sit-to-stand transitions by using a smartphone

The aim of this study is to analyze a common method to measure the acceleration of a daily activity pattern by using a smartphone. In this sense, a numerical approach is proposed to transform the relative acceleration signal, recorded by a triaxial accelerometer, into an acceleration referred to an inertial reference. The integration of this acceleration allows to determine the velocity and position with respect to an inertial reference. Two different kinematic parameters are suggested to characterize the profile of the velocity during the sit-to-stand and stand-to-sit transitions for Parkinson and control subjects. The results show that a dimensionless kinematic parameter, which is linked to the time of sit-to-stand and stand-to-sit transitions, has the potential to differentiate between Parkinson and control subjects.Peer ReviewedPreprin

Time-series analysis based on machine learning for occupational risk evaluation in public administration

Occupational diseases are currently a concerning problem for office workers, who spend long periods of time seated in static positions. Musculoskeletal disorders, specifically, have the highest prevalence among workers, contributing negatively by 17% to the Years Lived with Disability worldwide. This work is part of the PrevOccupAI project, which monitors office workers through wearable sensors and questionnaires, in order to provide them reports that bring to their attention some risk factors that can potentiate occupational diseases. During this work, a study with 40 subjects working in a real environment was carried out. After data pre-processing and synchronization, as it was only intended to analyze sitting data, the periods in which the participants were not seated were removed from the acquired signals. For this purpose, a machine learning model was developed, which uses features from the smartphone’s accelerometer signal to distinguish between sitting and walking. The best model reached an accuracy of 100.0%. Additionally, a model capable of partially predicting the participants’ answers to daily pain questionnaires was developed. Using the electromyography signals and personal information gathered from other questionnaires, it was possible to train a model that predicts if the subject reported pain or not, both at the beginning and end of the working day. Using the Random Forest algorithm, it was possible to achieve a mean accuracy of 86.3%. For each acquisition performed by the 40 participants, a relative ergonomic occupa- tional risk was assigned through variables that characterize postural variability. Using machine learning algorithms, models were trained to attempt to predict the modelled risk. A mean accuracy of 65.7% was achieved for the classification model, and a mean absolute error of 0.84 for the regression model.As doenças ocupacionais são, atualmente, um problema preocupante em trabalhadores de escritório, que passam muito tempo sentados em posições estáticas. As doenças muscu- loesqueléticas, especificamente, são as que têm maior prevalência entre os trabalhadores, contribuindo negativamente em 17% para os Anos Vividos com Incapacidade. Esta dissertação é parte do projeto PrevOccupAI, que monitoriza trabalhadores de escritório através de sensores e questionários, de forma a fornecer-lhes relatórios que cha- mem à sua atenção alguns dos fatores de risco que podem potenciar doenças ocupacionais. Durante este trabalho, foi realizado um estudo em 40 sujeitos a trabalhar em contexto real. Depois de pré-processamento e sincronização dos dados, como só se pretendia analisar dados de trabalhadores sentados, os períodos em que os participantes não estiveram sentados foram retirados dos sinais adquiridos. Para isso, foi desenvolvido um modelo de aprendizagem automática, que usa características do sinal do acelerómetro do telemóvel para distinguir entre sentado e a andar. O melhor modelo atingiu uma exatidão de 100,0%. Adicionalmente, foi desenvolvido um modelo capaz de prever parcialmente as respos- tas dos participantes a questionários diários de dor. Através dos sinais de eletromiografia e informação pessoal retirada de outros questionários, foi possível treinar um modelo que prevê se o sujeito reportou dor ou não, tanto no início como no fim do dia de trabalho. Utilizando o algoritmo de Floresta Aleatória, foi possível atingir uma exatidão média de 86,3%. A cada aquisição realizada pelos 40 participantes foi atribuído um risco ocupacional ergonómico relativo, através de variáveis que caracterizam a variabilidade postural. Uti- lizando algoritmos de aprendizagem automática, foram treinados modelos para tentar prever o risco modelado. Para o modelo de classificação, atingiu-se uma exatidão média de 65,7%, enquanto que para o modelo de regressão se conseguiu que o erro médio absoluto não ultrapassasse 0,84

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.This work was sponsored by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF) across projects RTC-2017-6321-1 AEI/FEDER, UE, TEC2016-76021-C2-2-R AEI/FEDER, UE and PID2019-107270RB-C21/AEI/10.13039/501100011033, UE

Assessment of Functional Activities in Individuals with Parkinson's Disease Using a Simple and Reliable Smartphone-Based Procedure

[EN] Parkinson's disease (PD) is a progressive neurodegenerative disorder leading to functional impairment. In order to monitor the progression of the disease and to implement individualized therapeutic approaches, functional assessments are paramount. The aim of this study was to determine the impact of PD on balance, gait, turn-to-sit and sit-to-stand by means of a single short-duration reliable test using a single inertial measurement unit embedded in a smartphone device. Study participants included 29 individuals with mild-to moderate PD (PG) and 31 age-matched healthy counterparts (CG). Functional assessment with FallSkip((R)) included postural control (i.e., Medial-Lateral (ML) and Anterior-Posterior (AP) displacements), gait (Vertical (V) and Medial-Lateral (ML) ranges), turn-to-sit (time) and sit-to-stand (power) tests, total time and gait reaction time. Our results disclosed a reliable procedure (intra-class correlation coefficient (ICC) = 0.58-0.92). PG displayed significantly larger ML and AP displacements during the postural test, a decrease in ML range while walking and a longer time needed to perform the turn-to-sit task than CG (p 0.05). In conclusion, people with mild-to-moderate PD exhibit impaired postural control, altered gait strategy and slower turn-to-sit performance than age-matched healthy people.This project (IMAMCJ/2020/1) was funded by Instituto Valenciano de Competitividad Empresarial (IVACE) and by the Valencian Regional Government (IVACE-GVA).Serra-Añó, P.; Pedrero, J.; Inglés, M.; Aguilar-Rodríguez, M.; Vargas-Villanueva, I.; Lopez Pascual, J. (2020). Assessment of Functional Activities in Individuals with Parkinson's Disease Using a Simple and Reliable Smartphone-Based Procedure. International Journal of Environmental research and Public Health (Online). 17(11):1-13. https://doi.org/10.3390/ijerph17114123S1131711Soh, S.-E., McGinley, J. L., Watts, J. J., Iansek, R., Murphy, A. T., Menz, H. B., … Morris, M. E. (2012). Determinants of health-related quality of life in people with Parkinson’s disease: a path analysis. Quality of Life Research, 22(7), 1543-1553. doi:10.1007/s11136-012-0289-1Mak, M. K. Y., & Wong-Yu, I. S. K. (2019). Exercise for Parkinson’s disease. Exercise on Brain Health, 1-44. doi:10.1016/bs.irn.2019.06.001Tysnes, O.-B., & Storstein, A. (2017). Epidemiology of Parkinson’s disease. Journal of Neural Transmission, 124(8), 901-905. doi:10.1007/s00702-017-1686-yKing, L. A., Wilhelm, J., Chen, Y., Blehm, R., Nutt, J., Chen, Z., … Horak, F. B. (2015). Effects of Group, Individual, and Home Exercise in Persons With Parkinson Disease. Journal of Neurologic Physical Therapy, 39(4), 204-212. doi:10.1097/npt.0000000000000101Haji Ghassemi, N., Hannink, J., Roth, N., Gaßner, H., Marxreiter, F., Klucken, J., & Eskofier, B. M. (2019). Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson’s Disease. Sensors, 19(14), 3103. doi:10.3390/s19143103Weiss, A., Herman, T., Mirelman, A., Shiratzky, S. S., Giladi, N., Barnes, L. L., … Hausdorff, J. M. (2019). The transition between turning and sitting in patients with Parkinson’s disease: A wearable device detects an unexpected sequence of events. Gait & Posture, 67, 224-229. doi:10.1016/j.gaitpost.2018.10.018Pham, M. H., Warmerdam, E., Elshehabi, M., Schlenstedt, C., Bergeest, L.-M., Heller, M., … Maetzler, W. (2018). Validation of a Lower Back «Wearable»-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson’s Disease and Older Adults in a Home-Like Environment. Frontiers in Neurology, 9. doi:10.3389/fneur.2018.00652González Rojas, H. A., Cuevas, P. C., Zayas Figueras, E. E., Foix, S. C., & Sánchez Egea, A. J. (2017). Time measurement characterization of stand-to-sit and sit-to-stand transitions by using a smartphone. Medical & Biological Engineering & Computing, 56(5), 879-888. doi:10.1007/s11517-017-1728-5Del Din, S., Godfrey, A., Mazzà, C., Lord, S., & Rochester, L. (2016). Free-living monitoring of Parkinson’s disease: Lessons from the field. Movement Disorders, 31(9), 1293-1313. doi:10.1002/mds.26718Galán-Mercant, A., Barón-López, F. J., Labajos-Manzanares, M. T., & Cuesta-Vargas, A. I. (2014). Reliability and criterion-related validity with a smartphone used in timed-up-and-go test. BioMedical Engineering OnLine, 13(1). doi:10.1186/1475-925x-13-156López-Pascual, J., Hurtado Abellán, J., Inglés, M., Espí-López, G., & Serra-Añó, P. (2018). P 151 – Reliability of variables measured with an Android device during a modified timed up and go test in patients with Alzheimer’s disease. Gait & Posture, 65, 484-485. doi:10.1016/j.gaitpost.2018.07.072Serra-Añó, P., Pedrero-Sánchez, J. F., Hurtado-Abellán, J., Inglés, M., Espí-López, G. V., & López-Pascual, J. (2019). Mobility assessment in people with Alzheimer disease using smartphone sensors. Journal of NeuroEngineering and Rehabilitation, 16(1). doi:10.1186/s12984-019-0576-yKerr, G. K., Worringham, C. J., Cole, M. H., Lacherez, P. F., Wood, J. M., & Silburn, P. A. (2010). Predictors of future falls in Parkinson disease. Neurology, 75(2), 116-124. doi:10.1212/wnl.0b013e3181e7b688Channa, A., Popescu, N., & Ciobanu, V. (2020). Wearable Solutions for Patients with Parkinson’s Disease and Neurocognitive Disorder: A Systematic Review. Sensors, 20(9), 2713. doi:10.3390/s20092713Hughes, A. J., Daniel, S. E., Kilford, L., & Lees, A. J. (1992). Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. Journal of Neurology, Neurosurgery & Psychiatry, 55(3), 181-184. doi:10.1136/jnnp.55.3.181Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). «Mini-mental state». Journal of Psychiatric Research, 12(3), 189-198. doi:10.1016/0022-3956(75)90026-6Dal Bello-Haas, V., Klassen, L., Sheppard, M. S., & Metcalfe, A. (2011). Psychometric Properties of Activity, Self-Efficacy, and Quality-of-Life Measures in Individuals with Parkinson Disease. Physiotherapy Canada, 63(1), 47-57. doi:10.3138/ptc.2009-08Zijlstra, W., & Hof, A. L. (2003). Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & Posture, 18(2), 1-10. doi:10.1016/s0966-6362(02)00190-xPrieto, T. E., Myklebust, J. B., Hoffmann, R. G., Lovett, E. G., & Myklebust, B. M. (1996). Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Transactions on Biomedical Engineering, 43(9), 956-966. doi:10.1109/10.532130Esser, P., Dawes, H., Collett, J., & Howells, K. (2009). IMU: Inertial sensing of vertical CoM movement. Journal of Biomechanics, 42(10), 1578-1581. doi:10.1016/j.jbiomech.2009.03.049Gordon, K. E., Ferris, D. P., & Kuo, A. D. (2009). Metabolic and Mechanical Energy Costs of Reducing Vertical Center of Mass Movement During Gait. Archives of Physical Medicine and Rehabilitation, 90(1), 136-144. doi:10.1016/j.apmr.2008.07.014Lindemann, U., Claus, H., Stuber, M., Augat, P., Muche, R., Nikolaus, T., & Becker, C. (2003). Measuring power during the sit-to-stand transfer. European Journal of Applied Physiology, 89(5), 466-470. doi:10.1007/s00421-003-0837-zAnsai, J. H., de Andrade, L. P., Rossi, P. G., Nakagawa, T. H., Vale, F. A. C., & Rebelatto, J. R. (2019). Differences in Timed Up and Go Subtasks Between Older People With Mild Cognitive Impairment and Mild Alzheimer’s Disease. Motor Control, 23(1), 1-12. doi:10.1123/mc.2017-0015Beauchet, O., Annweiler, C., Callisaya, M. L., De Cock, A.-M., Helbostad, J. L., Kressig, R. W., … Allali, G. (2016). Poor Gait Performance and Prediction of Dementia: Results From a Meta-Analysis. Journal of the American Medical Directors Association, 17(6), 482-490. doi:10.1016/j.jamda.2015.12.092Delval, A., Tard, C., & Defebvre, L. (2014). Why we should study gait initiation in Parkinson’s disease. Neurophysiologie Clinique/Clinical Neurophysiology, 44(1), 69-76. doi:10.1016/j.neucli.2013.10.127Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420-428. doi:10.1037/0033-2909.86.2.420Cicchetti, D. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4), 284-290. doi:10.1037/1040-3590.6.4.284Oliveira de Carvalho, A., Filho, A. S. S., Murillo-Rodriguez, E., Rocha, N. B., Carta, M. G., & Machado, S. (2018). Physical Exercise For Parkinson’s Disease: Clinical And Experimental Evidence. Clinical Practice & Epidemiology in Mental Health, 14(1), 89-98. doi:10.2174/1745017901814010089Tomlinson, C. L., Patel, S., Meek, C., Herd, C. P., Clarke, C. E., Stowe, R., … Ives, N. (2013). Physiotherapy versus placebo or no intervention in Parkinson’s disease. Cochrane Database of Systematic Reviews. doi:10.1002/14651858.cd002817.pub4Shen, X., Wong-Yu, I. S. K., & Mak, M. K. Y. (2015). Effects of Exercise on Falls, Balance, and Gait Ability in Parkinson’s Disease. Neurorehabilitation and Neural Repair, 30(6), 512-527. doi:10.1177/1545968315613447Post, B., Muslimovic, D., van Geloven, N., Speelman, J. D., Schmand, B., & de Haan, R. J. (2011). Progression and prognostic factors of motor impairment, disability and quality of life in newly diagnosed Parkinson’s disease. Movement Disorders, 26(3), 449-456. doi:10.1002/mds.23467Leddy, A. L., Crowner, B. E., & Earhart, G. M. (2011). Functional Gait Assessment and Balance Evaluation System Test: Reliability, Validity, Sensitivity, and Specificity for Identifying Individuals With Parkinson Disease Who Fall. Physical Therapy, 91(1), 102-113. doi:10.2522/ptj.20100113Park, J.-H., Kang, Y.-J., & Horak, F. B. (2015). What Is Wrong with Balance in Parkinson’s Disease? Journal of Movement Disorders, 8(3), 109-114. doi:10.14802/jmd.15018Frenklach, A., Louie, S., Koop, M. M., & Bronte-Stewart, H. (2008). Excessive postural sway and the risk of falls at different stages of Parkinson’s disease. Movement Disorders, 24(3), 377-385. doi:10.1002/mds.22358Doná, F., Aquino, C. C., Gazzola, J. M., Borges, V., Silva, S. M. C. A., Ganança, F. F., … Ferraz, H. B. (2016). Changes in postural control in patients with Parkinson’s disease: a posturographic study. Physiotherapy, 102(3), 272-279. doi:10.1016/j.physio.2015.08.009Takakusaki, K., Habaguchi, T., Ohtinata-Sugimoto, J., Saitoh, K., & Sakamoto, T. (2003). Basal ganglia efferents to the brainstem centers controlling postural muscle tone and locomotion: a new concept for understanding motor disorders in basal ganglia dysfunction. Neuroscience, 119(1), 293-308. doi:10.1016/s0306-4522(03)00095-2Błaszczyk, J. W., Orawiec, R., Duda-Kłodowska, D., & Opala, G. (2007). Assessment of postural instability in patients with Parkinson’s disease. Experimental Brain Research, 183(1), 107-114. doi:10.1007/s00221-007-1024-yCavagna, G. A., & Margaria, R. (1966). Mechanics of walking. Journal of Applied Physiology, 21(1), 271-278. doi:10.1152/jappl.1966.21.1.271Nguyen, H., Lebel, K., Boissy, P., Bogard, S., Goubault, E., & Duval, C. (2017). Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson’s disease using multiple inertial sensors. Journal of NeuroEngineering and Rehabilitation, 14(1). doi:10.1186/s12984-017-0241-2Hahn, M. E., & Chou, L.-S. (2003). Can motion of individual body segments identify dynamic instability in the elderly? Clinical Biomechanics, 18(8), 737-744. doi:10.1016/s0268-0033(03)00139-6Donelan, J. M., Shipman, D. W., Kram, R., & Kuo, A. D. (2004). Mechanical and metabolic requirements for active lateral stabilization in human walking. Journal of Biomechanics, 37(6), 827-835. doi:10.1016/j.jbiomech.2003.06.002Howell, D. R., Osternig, L. R., & Chou, L.-S. (2013). Dual-Task Effect on Gait Balance Control in Adolescents With Concussion. Archives of Physical Medicine and Rehabilitation, 94(8), 1513-1520. doi:10.1016/j.apmr.2013.04.015Akram, S., Frank, J. S., & Jog, M. (2013). Parkinson’s Disease and Segmental Coordination during Turning: I. Standing Turns. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques, 40(4), 512-519. doi:10.1017/s0317167100014591Verheyden, G., Willems, A.-M., Ooms, L., & Nieuwboer, A. (2007). Validity of the Trunk Impairment Scale as a Measure of Trunk Performance in People With Parkinson’s Disease. Archives of Physical Medicine and Rehabilitation, 88(10), 1304-1308. doi:10.1016/j.apmr.2007.06.772Mak, M. K. Y., & Hui-Chan, C. W. Y. (2005). The speed of sit-to-stand can be modulated in Parkinson’s disease. Clinical Neurophysiology, 116(4), 780-789. doi:10.1016/j.clinph.2004.12.017Inkster, L. M., & Eng, J. J. (2004). Postural control during a sit-to-stand task in individuals with mild Parkinson’s disease. Experimental Brain Research, 154(1), 33-38. doi:10.1007/s00221-003-1629-8Mak, M. K. Y., Levin, O., Mizrahi, J., & Hui-Chan, C. W. Y. (2003). Joint torques during sit-to-stand in healthy subjects and people with Parkinson’s disease. Clinical Biomechanics, 18(3), 197-206. doi:10.1016/s0268-0033(02)00191-2Trujillo, J. P., Gerrits, N. J. H. M., Vriend, C., Berendse, H. W., van den Heuvel, O. A., & van der Werf, Y. D. (2015). Impaired planning in P arkinson’s disease is reflected by reduced brain activation and connectivity. Human Brain Mapping, 36(9), 3703-3715. doi:10.1002/hbm.22873Nocera, J. R., Stegemöller, E. L., Malaty, I. A., Okun, M. S., Marsiske, M., & Hass, C. J. (2013). Using the Timed Up & Go Test in a Clinical Setting to Predict Falling in Parkinson’s Disease. Archives of Physical Medicine and Rehabilitation, 94(7), 1300-1305. doi:10.1016/j.apmr.2013.02.020Byl, N., R, H., R, R., & D, B. (2018). Is the timed up and go (TUG) sensitive to differentiating patients with mild to moderate PD compared to age matched controls: a descriptive pilot study. International Physical Medicine & Rehabilitation Journal, 3(1). doi:10.15406/ipmrj.2018.03.00094Barry, E., Galvin, R., Keogh, C., Horgan, F., & Fahey, T. (2014). Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta- analysis. BMC Geriatrics, 14(1). doi:10.1186/1471-2318-14-14Cohen, R. G., Nutt, J. G., & Horak, F. B. (2017). Recovery from Multiple APAs Delays Gait Initiation in Parkinson’s Disease. Frontiers in Human Neuroscience, 11. doi:10.3389/fnhum.2017.00060Bloem, B. R., Hausdorff, J. M., Visser, J. E., & Giladi, N. (2004). Falls and freezing of gait in Parkinson’s disease: A review of two interconnected, episodic phenomena. Movement Disorders, 19(8), 871-884. doi:10.1002/mds.2011

Smart Button: A wearable system for assessing mobility in elderly

Abstract—Continuous advances in sensors, semiconductors, wireless networks, mobile and cloud computing enable the development of integrated wearable computing systems for continuous health monitoring. These systems can be used as a part of diagnostic procedures, in the optimal maintenance of chronic conditions, in the monitoring of adherence to treatment guidelines, and for supervised recovery. In this paper, we describe a wearable system called Smart Button designed to assess mobility of elderly. The Smart Button is easily mounted on the chest of an individual and currently quantifies the Timed-Up-and-Go and 30-Second Chair Stand tests. These two tests are routinely used to assess mobility, balance, strength of the lower extremities, and fall risk of elderly and people with Parkinson’s disease. The paper describes the design of the Smart Button, parameters used to quantify the tests, signal processing used to extract the parameters, and integration of the Smart Button into a broader mHealth system. Keywords—mobile sensing; health monitoring; wearable devices; timed-up-and-go test; 30-second chair stand test. I

Context-aware home monitoring system for Parkinson's disease patietns : ambient and werable sensing for freezing of gait detection

Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Technische Universiteit Eindhoven. This PhD Thesis has been developed in the framework of, and according to, the rules of the Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments EMJD ICE [FPA no. 2010-0012]Parkinson’s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOG are aggravated mobility and higher affinity to falls, which have a direct effect on the quality of life of the individual. There does not exist completely effective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the floor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal effectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this specific user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection could be achieved by using contextual information of the patient’s position, orientation, basic posture and movement on a semantically annotated two-dimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsoft Kinect cameras placed in the patient’s home that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development efforts involved the production of software modules (for position tracking, orientation tracking, activity recognition) that run on top of the middle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this software module also provides mapping and semantic annotation of FOG specific zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one specific person - the patient. To enable tracking of the patient between different non-overlapped cameras in the distributed system, a new re-identification approach based on appearance model learning with one-class Support Vector Machine (SVM) was developed. Evaluation of the re-identification method was conducted on a 16 people dataset in a laboratory environment. Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera. We devised method to fuse position tracking information from the vision system and inertial data from the smartphone in order to obtain patient’s 2D pose estimation on the scene map. Additionally, a method for the estimation of the position of the smartphone on the waist of the patient was proposed. Position and orientation estimation accuracy were evaluated on a 12 people dataset. Finally, having available positional, orientation and height information, a new seven-class activity classification was realized using a hierarchical classifier that combines height-based posture classifier with translational and rotational SVM movement classifiers. Each of the SVM movement classifiers and the joint hierarchical classifier were evaluated in the laboratory experiment with 8 healthy persons. The final context-based FOG detection algorithm uses activity information and spatial context information in order to confirm or disprove FOG detected by the current state-of-the-art FOG detection algorithm (which uses only wearable sensor data). A dataset with home data of 3 PD patients was produced using two Kinect cameras and a smartphone in synchronized recording. The new context-based FOG detection algorithm and the wearable-only FOG detection algorithm were both evaluated with the home dataset and their results were compared. The context-based algorithm very positively influences the reduction of false positive detections, which is expressed through achieved higher specificity. In some cases, context-based algorithm also eliminates true positive detections, reducing sensitivity to the lesser extent. The final comparison of the two algorithms on the basis of their sensitivity and specificity, shows the improvement in the overall FOG detection achieved with the new context-aware home system.Esta tesis propone el uso de la actividad y el contexto espacial de una persona como medio para mejorar la detección de episodios de FOG (Freezing of gait) durante el seguimiento en el domicilio. La tesis describe el diseño, implementación de algoritmos y evaluación de un sistema doméstico distribuido para detección de FOG basado en varias cámaras y un único sensor de marcha inercial en la cintura del paciente. Mediante de la observación detallada de los datos caseros recopilados de 17 pacientes con EP, nos dimos cuenta de que se puede lograr una solución novedosa para la detección de FOG mediante el uso de información contextual de la posición del paciente, orientación, postura básica y movimiento anotada semánticamente en un mapa bidimensional (2D) del entorno interior. Imaginamos el futuro sistema de consciencia del contexto como una red de cámaras Microsoft Kinect colocadas en el hogar del paciente, que interactúa con un sensor de inercia portátil en el paciente (teléfono inteligente). Al constituirse la plataforma del sistema a partir de hardware comercial disponible, los esfuerzos de desarrollo consistieron en la producción de módulos de software (para el seguimiento de la posición, orientación seguimiento, reconocimiento de actividad) que se ejecutan en la parte superior del sistema operativo del servidor de puerta de enlace de casa. El componente principal del sistema que tuvo que desarrollarse es la aplicación Kinect para seguimiento de la posición y la altura de varias personas, según la entrada en forma de punto 3D de datos en la nube. Además del seguimiento de posición, este módulo de software también proporciona mapeo y semántica. anotación de zonas específicas de FOG en la escena frente al Kinect. Se supone que una instancia de la aplicación de seguimiento de visión se ejecuta para cada sensor Kinect en el sistema, produciendo un número potencialmente alto de pistas simultáneas. En cualquier momento, el sistema tiene que rastrear a una persona específica - el paciente. Para habilitar el seguimiento del paciente entre diferentes cámaras no superpuestas en el sistema distribuido, se desarrolló un nuevo enfoque de re-identificación basado en el aprendizaje de modelos de apariencia con one-class Suport Vector Machine (SVM). La evaluación del método de re-identificación se realizó con un conjunto de datos de 16 personas en un entorno de laboratorio. Dado que la orientación del paciente en el espacio interior fue reconocida como una parte importante del contexto, el sistema necesitaba la capacidad de estimar la orientación de la persona, expresada en el marco de la escena 2D en la que la cámara sigue al paciente. Diseñamos un método para fusionar la información de seguimiento de posición del sistema de visión y los datos de inercia del smartphone para obtener la estimación de postura 2D del paciente en el mapa de la escena. Además, se propuso un método para la estimación de la posición del Smartphone en la cintura del paciente. La precisión de la estimación de la posición y la orientación se evaluó en un conjunto de datos de 12 personas. Finalmente, al tener disponible información de posición, orientación y altura, se realizó una nueva clasificación de actividad de seven-class utilizando un clasificador jerárquico que combina un clasificador de postura basado en la altura con clasificadores de movimiento SVM traslacional y rotacional. Cada uno de los clasificadores de movimiento SVM y el clasificador jerárquico conjunto se evaluaron en el experimento de laboratorio con 8 personas sanas. El último algoritmo de detección de FOG basado en el contexto utiliza información de actividad e información de texto espacial para confirmar o refutar el FOG detectado por el algoritmo de detección de FOG actual. El algoritmo basado en el contexto influye muy positivamente en la reducción de las detecciones de falsos positivos, que se expresa a través de una mayor especificidadPostprint (published version

Context-aware home monitoring system for Parkinson's disease patietns : ambient and werable sensing for freezing of gait detection

Parkinson’s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOG are aggravated mobility and higher affinity to falls, which have a direct effect on the quality of life of the individual. There does not exist completely effective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the floor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal effectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this specific user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection could be achieved by using contextual information of the patient’s position, orientation, basic posture and movement on a semantically annotated two-dimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsoft Kinect cameras placed in the patient’s home that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development efforts involved the production of software modules (for position tracking, orientation tracking, activity recognition) that run on top of the middle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this software module also provides mapping and semantic annotation of FOG specific zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one specific person - the patient. To enable tracking of the patient between different non-overlapped cameras in the distributed system, a new re-identification approach based on appearance model learning with one-class Support Vector Machine (SVM) was developed. Evaluation of the re-identification method was conducted on a 16 people dataset in a laboratory environment. Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera. We devised method to fuse position tracking information from the vision system and inertial data from the smartphone in order to obtain patient’s 2D pose estimation on the scene map. Additionally, a method for the estimation of the position of the smartphone on the waist of the patient was proposed. Position and orientation estimation accuracy were evaluated on a 12 people dataset. Finally, having available positional, orientation and height information, a new seven-class activity classification was realized using a hierarchical classifier that combines height-based posture classifier with translational and rotational SVM movement classifiers. Each of the SVM movement classifiers and the joint hierarchical classifier were evaluated in the laboratory experiment with 8 healthy persons. The final context-based FOG detection algorithm uses activity information and spatial context information in order to confirm or disprove FOG detected by the current state-of-the-art FOG detection algorithm (which uses only wearable sensor data). A dataset with home data of 3 PD patients was produced using two Kinect cameras and a smartphone in synchronized recording. The new context-based FOG detection algorithm and the wearable-only FOG detection algorithm were both evaluated with the home dataset and their results were compared. The context-based algorithm very positively influences the reduction of false positive detections, which is expressed through achieved higher specificity. In some cases, context-based algorithm also eliminates true positive detections, reducing sensitivity to the lesser extent. The final comparison of the two algorithms on the basis of their sensitivity and specificity, shows the improvement in the overall FOG detection achieved with the new context-aware home system.Esta tesis propone el uso de la actividad y el contexto espacial de una persona como medio para mejorar la detección de episodios de FOG (Freezing of gait) durante el seguimiento en el domicilio. La tesis describe el diseño, implementación de algoritmos y evaluación de un sistema doméstico distribuido para detección de FOG basado en varias cámaras y un único sensor de marcha inercial en la cintura del paciente. Mediante de la observación detallada de los datos caseros recopilados de 17 pacientes con EP, nos dimos cuenta de que se puede lograr una solución novedosa para la detección de FOG mediante el uso de información contextual de la posición del paciente, orientación, postura básica y movimiento anotada semánticamente en un mapa bidimensional (2D) del entorno interior. Imaginamos el futuro sistema de consciencia del contexto como una red de cámaras Microsoft Kinect colocadas en el hogar del paciente, que interactúa con un sensor de inercia portátil en el paciente (teléfono inteligente). Al constituirse la plataforma del sistema a partir de hardware comercial disponible, los esfuerzos de desarrollo consistieron en la producción de módulos de software (para el seguimiento de la posición, orientación seguimiento, reconocimiento de actividad) que se ejecutan en la parte superior del sistema operativo del servidor de puerta de enlace de casa. El componente principal del sistema que tuvo que desarrollarse es la aplicación Kinect para seguimiento de la posición y la altura de varias personas, según la entrada en forma de punto 3D de datos en la nube. Además del seguimiento de posición, este módulo de software también proporciona mapeo y semántica. anotación de zonas específicas de FOG en la escena frente al Kinect. Se supone que una instancia de la aplicación de seguimiento de visión se ejecuta para cada sensor Kinect en el sistema, produciendo un número potencialmente alto de pistas simultáneas. En cualquier momento, el sistema tiene que rastrear a una persona específica - el paciente. Para habilitar el seguimiento del paciente entre diferentes cámaras no superpuestas en el sistema distribuido, se desarrolló un nuevo enfoque de re-identificación basado en el aprendizaje de modelos de apariencia con one-class Suport Vector Machine (SVM). La evaluación del método de re-identificación se realizó con un conjunto de datos de 16 personas en un entorno de laboratorio. Dado que la orientación del paciente en el espacio interior fue reconocida como una parte importante del contexto, el sistema necesitaba la capacidad de estimar la orientación de la persona, expresada en el marco de la escena 2D en la que la cámara sigue al paciente. Diseñamos un método para fusionar la información de seguimiento de posición del sistema de visión y los datos de inercia del smartphone para obtener la estimación de postura 2D del paciente en el mapa de la escena. Además, se propuso un método para la estimación de la posición del Smartphone en la cintura del paciente. La precisión de la estimación de la posición y la orientación se evaluó en un conjunto de datos de 12 personas. Finalmente, al tener disponible información de posición, orientación y altura, se realizó una nueva clasificación de actividad de seven-class utilizando un clasificador jerárquico que combina un clasificador de postura basado en la altura con clasificadores de movimiento SVM traslacional y rotacional. Cada uno de los clasificadores de movimiento SVM y el clasificador jerárquico conjunto se evaluaron en el experimento de laboratorio con 8 personas sanas. El último algoritmo de detección de FOG basado en el contexto utiliza información de actividad e información de texto espacial para confirmar o refutar el FOG detectado por el algoritmo de detección de FOG actual. El algoritmo basado en el contexto influye muy positivamente en la reducción de las detecciones de falsos positivos, que se expresa a través de una mayor especificida

The Emerging Wearable Solutions in mHealth

The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed