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This article is part of the Topical Collection on Systems Medicine
The recent growth in the wearable sensor market has stimulated new opportunities within the domain of Ambient Assisted Living, providing unique methods of collecting occupant information. This approach leverages contemporary wearable technology, Google Glass, to facilitate a unique first-person view of the occupants immediate environment. Machine vision techniques are employed to determine an occupant’s location via environmental object detection. This method provides additional secondary benefits such as first person tracking within the environment and lack of required sensor interaction to determine occupant location. Object recognition is performed using the Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features algorithm with a K-Nearest Neighbour matcher to match the saved key-points of the objects to the scene. To validate the approach, an experimental set-up consisting of three ADL routines, each containing at least ten activities, ranging from drinking water to making a meal were considered. Ground truth was obtained from manually annotated video data and the approach was previously benchmarked against a common method of indoor localisation that employs dense sensor placement in order to validate the approach resulting in a recall, precision, and F-measure of 0.82, 0.96, and 0.88 respectively. This paper will go on to assess to the viability of applying the solution to differing environments, both in terms of performance and along with a qualitative analysis on the practical aspects of installing such a system within differing environments.
Brush A.J.B., Lee B, Mahajan R, Agarwal S, Saroiu S, Dixon C. Home Automation in the Wild: Challenges and Opportunities. CHI Conference on Human Factors in Computing Systems, pp. 2115–2124; 2011. doi: 10.1145/1978942.1979249.
Cardinaux F, Deepayan B, Charith A, Hawley M.S, Mark S, Bhowmik D, Abhayaratne C. Video Based Technology for Ambient Assisted Living : A review of the literature. Journal of Ambient Intelligence and Smart Environments (JAISE) 2011;1364(3):253–269. doi: 10.3233/AIS-2011-0110.
Cheng J, Leng C, Wu J, Cui H, Lu H. Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction. Computer Vision and Pattern Recognition, pp. 1–8. IEEE Comput. Soc, Columbus, OH; 2014. doi: 10.1109/CVPR.2014.8.
Chintalapudi K, Padmanabha Iyer A, Padmanabhan VN. Indoor localization without the pain. 16th Annual International Conference on Mobile Computing and Networking - (MobiCom ’10), p. 173; 2010. doi: 10.1145/1859995.1860016.
Fanqing M, Funcheng Y. A Tracking Algorithm Based on ORB. International Conference on Mechatronic Sciences, Electric Engineering and Computer, 1, pp. 1187–1190. IEEE, Shengyang ; 2013. doi: 10.1109/MEC.2013.6885245.
Ha K, Chen Z, Hu W, Richter W, Pillai P, Satyanarayanan M. Towards wearable cognitive assistance. Proceedings of the 12th annual international conference on Mobile systems, applications, and services, pp. 68–81. ACM ; 2014. doi: 10.1145/2594368.2594383.
Hightower J, Borriello G. Location Systems for Ubiquitous Computing. Computer 2001;34(8):57–66. CrossRef
Konstantinidis E, Antoniou P, Bamparopoulos G, Bamidis P. A lightweight framework for transparent cross platform communication of contoller data in ambient assisted living environments. Information Sciences: an International Journal 2015;300(C):124–139. doi: 10.1016/j.ins.2014.10.070. CrossRef
Konstantinidis E, Mpillis A, Plotegher L, Conti G, Bamdidis P. Indoor Location IoT Analytics ”in the wild”: Active and Healthy Ageing Cases. XIV Mediterranean Conference on Medical and Biological Engineering and Computing, pp. 1225–1230. Springer International Publishing; 2016. doi: 10.1007/978-3-319-32703-7_236.
Leotta F, Mecella M. PLaTHEA: a marker-less people localization and tracking system for home automation. Software - Practice and Experience 2014;39(7):661–699. doi: 10.1002/spe.
LiKamWa R, Wang Z, Carroll A, Lin FX, Zhong L. Draining our glass. Proceedings of 5th Asia-Pacific Workshop on Systems, pp. 1–7. ACM; 2014. doi: 10.1145/2637166.2637230.
Okeyo G., Chen L. Wang, H.: An Agent-mediated Ontology-based Approach for Composite Activity Recognition in Smart Homes. Journal of Universal Computer Science 2013;19(17):2577–2597.
Orrite C, Soler J, Rodríguez M, Herrero E, Casas R. Image-based Location Recognition and Scenario Modelling. International Conference on Computer Vision Theory and Applications, pp. 216–221; 2015. doi: 10.5220/0005352702160221.
Owen C, Xiao FXF, Middlin P. What is the best fiducial?. The First IEEE International Workshop Agumented Reality Toolkit 2002;15(11):3317. doi: 10.1109/ART.2002.1107021.
Přibyl B, Chalmers A, Zemčík P. Feature Point Detection under Extreme Lighting Conditions. Conference on Computer Graphics, May, pp. 156–163; 2012.
Rahal Y, Pigot H, Mabilleau P. Location estimation in a smart home: System implementation and evaluation using experimental data. International Journal of Telemedicine and Applications 2008;2008(4):9. doi: 10.1155/2008/142803.
Rivera-rubio J., Alexiou I., Bharath A., Secoli R., Dickens L., Lupu EC. Associating locations from wearable cameras. British machine vision conference, pp. 1–13; 2014.
Rublee E., Rabaud V., Konolige K., Bradski G. ORB: an efficient alternative to SIFT or SURF. International conference on computer vision. Barcelona: IEEE; 2011. p. 2564– 2571.
Shewell C, Medina-Quero J, Espinilla M, Nugent C, Donnelly M, Wang H. 2016. Fiducial Marker and Object Interaction in Activities of Daily Living using Wearable Vision Sensor International Journal of Communication Systems. doi: 10.1002/dac.3223.
Shewell C., Nugent C., Donnelly M., Wang H. indoor localisation through object detection on Real-Time video implementing a single wearable camera. E. Kyriacou, S. Christofides, C. Pattichis (eds.) Mediterranean Conference on Medical and Biological Engineering and Computing, pp. 1231?1236. Springer International Publishing, Paphos, Cyprus; 2016.
The College of Optometrists: Britain’s Eye Health in Focus. 2013.
Verma D., Kakkar N. 2014. Mehan, n.: comparison of Brute-Force and K-D tree algorithm international journal of advanced research in computer and communication engineering 3(1).
Viola P., Jones MJ . Robust Real-Time face detection. Int J Comput Vis 2004;57(2):137–154. CrossRef
Zeb A., Ullah S., Rabbi I. indoor Vision-Based auditory assistance for blind people in semi controlled environments. Image processing theory, tools and applications, pp. 1–6; 2014.
- Indoor localisation through object detection within multiple environments utilising a single wearable camera
- Springer Berlin Heidelberg
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