Recognition of activities of daily living in Enhanced Living Environments

Enhanced Living Environments consider the recognition of the Activities of Daily Living (ADLs) being performed by users as first step in the aid plans. Some works proved the information about the objects with which a person interacts robustly characterizes the ADL?s identity. However, designing aid plans based on these raw data is a very complicated task, as an expert in both technology and occupational sciences is required. In addition, the plans produced by these experts are not platformindependent, to be closely linked to the hardware characteristics. Therefore, the aim of this paper is to design a two-phase solution capable of acquiring data from users, and of extracting information about the ADL being performed to trigger the execution of the proper aid plans. This solution disengages the technological and occupational domains, so that aid plans could be applied to any environment. Moreover, an experimental validation is conducted in order to validate the proposed technology as a valid solution for ADL recognition.

[1]  Julián Urbano,et al.  Patterns as objects to manage knowledge in software development organizations , 2012 .

[2]  S. Katz,et al.  STUDIES OF ILLNESS IN THE AGED. THE INDEX OF ADL: A STANDARDIZED MEASURE OF BIOLOGICAL AND PSYCHOSOCIAL FUNCTION. , 1963, JAMA.

[3]  Matthai Philipose,et al.  Hands-on RFID: wireless wearables for detecting use of objects , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[4]  Ľ.,et al.  Use of Uniform Terminology by Occupational Therapists , 2008 .

[5]  Albrecht Schmidt,et al.  Enabling implicit human computer interaction: a wearable RFID-tag reader , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[6]  Gwenn Englebienne,et al.  An activity monitoring system for elderly care using generative and discriminative models , 2010, Personal and Ubiquitous Computing.

[7]  Chris D. Nugent,et al.  Using duration to learn activities of daily living in a smart home environment , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[8]  Ramón Alcarria,et al.  Resolving coordination challenges in distributed mobile service executions , 2014, Int. J. Web Grid Serv..

[9]  Deva Ramanan,et al.  Detecting activities of daily living in first-person camera views , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Carmen D Dirksen,et al.  Literature review on monitoring technologies and their outcomes in independently living elderly people , 2015, Disability and rehabilitation. Assistive technology.

[11]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[12]  Josef Hallberg,et al.  Assessing the impact of individual sensor reliability within smart living environments , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[13]  Yoshiharu Yonezawa,et al.  A daily living activity remote monitoring system for solitary elderly people , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Mann Oo. Hay.  Emotion recognition in human-computer interaction , 2012 .

[15]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[16]  Sajal K. Das,et al.  Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) , 2004 .

[17]  Giancarlo Fortino,et al.  Agent-oriented smart objects development , 2012, Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[18]  Diego Sánchez de Rivera,et al.  Building unobtrusive wearable devices: an ergonomic cybernetic glove , 2016, J. Internet Serv. Inf. Secur..

[19]  Huiru Zheng,et al.  Human Activity Detection in Smart Home Environment with Self-Adaptive Neural Networks , 2008, 2008 IEEE International Conference on Networking, Sensing and Control.

[20]  Bingbing Ni,et al.  RGBD-HuDaAct: A color-depth video database for human daily activity recognition , 2011, ICCV Workshops.

[21]  Darko Huljenic,et al.  A Survey on User Interaction Mechanisms for Enhanced Living Environments , 2015, ICT Innovations.

[22]  Gerhard Fischer,et al.  User Modeling in Human–Computer Interaction , 2001, User Modeling and User-Adapted Interaction.

[23]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[24]  Tobias Nef,et al.  Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data , 2012, Sensors.

[25]  Eliathamby Ambikairajah,et al.  Robust Sounds of Activities of Daily Living Classification in Two-Channel Audio-Based Telemonitoring , 2013, International journal of telemedicine and applications.

[26]  Bernt Schiele,et al.  ADL recognition based on the combination of RFID and accelerometer sensing , 2008, 2008 Second International Conference on Pervasive Computing Technologies for Healthcare.

[27]  Borja Bordel Sánchez,et al.  T4AI: A system for monitoring people based on improved wearable devices , 2016 .

[28]  Juan Ignacio Vázquez,et al.  RFIDGlove: A Wearable RFID Reader , 2009, 2009 IEEE International Conference on e-Business Engineering.

[29]  Saso Koceski,et al.  ICT Innovations 2015-Emerging Technologies for Better Living , 2016 .

[30]  Ramón Alcarria,et al.  TF4SM: A Framework for Developing Traceability Solutions in Small Manufacturing Companies , 2015, Sensors.

[31]  Borja Bordel,et al.  Towards a Wireless and Low-Power Infrastructure for Representing Information Based on E-Paper Displays , 2017 .

[32]  Bart Vanrumste,et al.  Monitoring activities of daily living using Wireless Acoustic Sensor Networks in clean and noisy conditions , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[33]  Hajime Takechi,et al.  Relative Preservation of Advanced Activities in Daily Living among Patients with Mild-to-Moderate Dementia in the Community and Overview of Support Provided by Family Caregivers , 2012, International journal of Alzheimer's disease.

[34]  Ramón Alcarria,et al.  Enhancing Evacuation Plans with a Situation Awareness System Based on End-User Knowledge Provision , 2014, Sensors.

[35]  Héctor Pomares,et al.  Daily living activity recognition based on statistical feature quality group selection , 2012, Expert Syst. Appl..

[36]  Andreas Hein,et al.  A novel approach for discovering human behavior patterns using unsupervised methods , 2014, Zeitschrift für Gerontologie und Geriatrie.

[37]  Araceli Sanchis,et al.  Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors , 2013, Sensors.