Introduction to the Model of the Active Assistance System for Elder and Disabled People

In this article we present assumptions for development of novel model of active system that can assist elder and disabled people. In the following sections we discuss literature and propose a structure of decision support and data processing on levels: voice and speech processing, image processing based on proposed descriptors, routing and positioning. For these aspects pros and cons that can be faced in the development process are described with potential preventive actions.

[1]  Krystian Lapa,et al.  A new approach to design of control systems using genetic programming , 2015, Inf. Technol. Control..

[2]  Germanas Budnikas,et al.  A Model for an Aggression Discovery Through Person Online Behavior , 2015, CISIM.

[3]  Jacek Mandziuk,et al.  An Automatically Generated Evaluation Function in General Game Playing , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[4]  David R Bassett,et al.  2011 Compendium of Physical Activities: a second update of codes and MET values. , 2011, Medicine and science in sports and exercise.

[5]  Dimitrios Tzovaras,et al.  Spatiotemporal analysis of human activities for biometric authentication , 2012, Comput. Vis. Image Underst..

[6]  Damian Słota,et al.  RECONSTRUCTION OF THE BOUNDARY CONDITION FOR THE HEAT CONDUCTION EQUATION OF FRACTIONAL ORDER , 2015 .

[7]  Christian Napoli,et al.  A Cloud-Distributed GPU Architecture for Pattern Identification in Segmented Detectors Big-Data Surveys , 2016, Comput. J..

[8]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Donghoon Lee,et al.  Fast and Accurate Head Pose Estimation via Random Projection Forests , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Mohanraj Karunanithi,et al.  Review of accelerometry for determining daily activity among elderly patients. , 2011, Archives of physical medicine and rehabilitation.

[11]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[12]  Robert Bergevin,et al.  Semantic human activity recognition: A literature review , 2015, Pattern Recognit..

[13]  Jake K. Aggarwal,et al.  Human activity recognition from 3D data: A review , 2014, Pattern Recognit. Lett..

[14]  Damian Slota,et al.  Application of Intelligent Algorithm to Solve the Fractional Heat Conduction Inverse Problem , 2015, ICIST.

[15]  Tim Dallas,et al.  Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer , 2014, IEEE Transactions on Biomedical Engineering.

[16]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[17]  Angelo M. Sabatini,et al.  Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers , 2010, Sensors.

[18]  Billur Barshan,et al.  Detecting Falls with Wearable Sensors Using Machine Learning Techniques , 2014, Sensors.

[19]  Venet Osmani,et al.  Human activity recognition in pervasive health-care: Supporting efficient remote collaboration , 2008, J. Netw. Comput. Appl..

[20]  Enamul Hoque,et al.  AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[21]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[22]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[23]  Paul J. M. Havinga,et al.  A Survey of Online Activity Recognition Using Mobile Phones , 2015, Sensors.

[24]  Leszek Rutkowski,et al.  A new algorithm for identity verification based on the analysis of a handwritten dynamic signature , 2016, Appl. Soft Comput..

[25]  Robertas Damasevicius,et al.  A Prototype SSVEP Based Real Time BCI Gaming System , 2016, Comput. Intell. Neurosci..

[26]  William G. Griswold,et al.  Mobility Detection Using Everyday GSM Traces , 2006, UbiComp.

[27]  Edward D. Lemaire,et al.  Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients , 2015, PloS one.

[28]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[29]  Du Tran,et al.  Human Activity Recognition with Metric Learning , 2008, ECCV.

[30]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[31]  Marcin Korytkowski,et al.  Secure Representation of Images Using Multi-layer Compression , 2015, ICAISC.

[32]  Venu Govindaraju,et al.  A generative framework to investigate the underlying patterns in human activities , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[33]  Christian Napoli,et al.  A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent , 2016, Int. J. Appl. Math. Comput. Sci..

[34]  Yuwei Chen,et al.  Human Behavior Cognition Using Smartphone Sensors , 2013, Sensors.

[35]  Cem Ersoy,et al.  A Review and Taxonomy of Activity Recognition on Mobile Phones , 2013 .

[36]  Michel Vacher,et al.  Improving Supervised Classification of Activities of Daily Living Using Prior Knowledge , 2011, Int. J. E Health Medical Commun..

[37]  Robertas Damasevicius,et al.  Domain Ontology-Based Generative Component Design Using Feature Diagrams and Meta-programming Techniques , 2008, ECSA.

[38]  Jun Yang,et al.  Toward physical activity diary: motion recognition using simple acceleration features with mobile phones , 2009, IMCE '09.

[39]  Mirco Musolesi,et al.  Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application , 2008, SenSys '08.

[40]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

[41]  Mario Cannataro,et al.  Protein-to-protein interactions: Technologies, databases, and algorithms , 2010, CSUR.

[42]  Mi Zhang,et al.  USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors , 2012, UbiComp.

[43]  Steven Dubowsky,et al.  An Adaptive Shared Control System for an Intelligent Mobility Aid for the Elderly , 2003, Auton. Robots.

[44]  Guang-Zhong Yang,et al.  Sensor Positioning for Activity Recognition Using Wearable Accelerometers , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[45]  Marta Wlodarczyk-Sielicka,et al.  Selection of SOM parameters for the needs of clusterization of data obtained by interferometric methods , 2015, 2015 16th International Radar Symposium (IRS).

[46]  Weihua Sheng,et al.  Motion- and location-based online human daily activity recognition , 2011, Pervasive Mob. Comput..

[47]  Ahmed Kattan,et al.  Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body , 2015, PloS one.

[48]  Venu Govindaraju,et al.  Behavioural biometrics: a survey and classification , 2008, Int. J. Biom..

[49]  Marta Wlodarczyk-Sielicka,et al.  Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process , 2014, RSEISP.

[50]  Marcin Zalasinski,et al.  On-line signature verification using vertical signature partitioning , 2014, Expert Syst. Appl..

[51]  Jacek Mandziuk,et al.  Two-phase multi-swarm PSO and the dynamic vehicle routing problem , 2014, 2014 IEEE Symposium on Computational Intelligence for Human-like Intelligence (CIHLI).

[52]  Marcin Korytkowski,et al.  Fast image classification by boosting fuzzy classifiers , 2016, Inf. Sci..

[53]  Aini Hussain,et al.  Sudden Event Recognition: A Survey , 2013, Sensors.

[54]  Juha Röning,et al.  Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data , 2012, Int. J. Interact. Multim. Artif. Intell..