![](bg6.png)
coach to motivate elderly individuals to walk. In Per-
vasive Computing Technologies for Healthcare, 2009.
PervasiveHealth 2009. 3rd International Conference
on, pages 1–7. IEEE.
Atwal, S., Porter, J., and MacDonald, P. (2002). Cardiovas-
cular effects of strenuous exercise in adult recreational
hockey: The hockey heart study. CMAJ.
BISFed (2016). Bisfed - boccia international sports federa-
tion. http://www.bisfed.com/, [Online; accessed 2017-
04-10].
Blair, S. N., Brodney, S., et al. (1999). Effects of physi-
cal inactivity and obesity on morbidity and mortality:
current evidence and research issues. Medicine and
science in sports and exercise, 31:S646–S662.
Charlon, Y., Bourennane, W., Bettahar, F., and Campo, E.
(2013). Activity monitoring system for elderly in a
context of smart home. IRBM, 34(1):60–63.
Choudhury, T., Consolvo, S., Harrison, B., Hightower, J.,
LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bor-
dello, G., Hemingway, B., et al. (2008). The mobile
sensing platform: An embedded activity recognition
system. IEEE Pervasive Computing, 7(2).
da Costa, M. J. M. P. et al. (2015). Mobile real-time classi-
fication of activities of daily-living in post-stroke pa-
tients.
da Silva, P. R. D. C. (2013). Smartphone gesture learning.
Ekman, P. and Friesen, W. V. (1978). Facial Action Coding
System: A Technique for the Measurement of Facial
Movement.
Eleni, T. (2015). Gesture Recognition with a Convolutional
Long Short Term Memory Recurrent Neural Network.
PhD thesis.
EverThere, A. (2017). How everthere works. https://www.
att.com/att/InnovationStore/images/products/everthere
/docs/HowItWorks.pdf, [Online; accessed 2017-04-
07].
Figueira, C., Matias, R., and Gamboa, H. (2015). Body
location independent activity monitoring. In BIOSIG-
NALS, page 8.
Fong, D. T.-P., Yam, K.-Y., Chu, V. W.-S., Cheung, R. T.-H.,
and Chan, K.-M. (2012). Upper limb muscle fatigue
during prolonged boccia games with underarm throw-
ing technique. Sports Biomechanics, 11(4):441–451.
Fosler-Lussier, E. (1998). Markov models and hidden
markov models: a brief tutorial. International Com-
puter Science Institute.
Freixo, R. A. E. (2015). Electromyography and inertial
sensor-based gesture detection and control.
Gociety (2016). Gociety solutions. http://www.gociety so-
lutions.com/, [Online; accessed 2017-04-11].
Guo, F., Li, Y., Kankanhalli, M. S., and Brown, M. S.
(2013). An evaluation of wearable activity monitoring
devices. In Proceedings of the 1st ACM international
workshop on Personal data meets distributed multi-
media, pages 31–34. ACM.
Kotsia, I. and Pitas, I. (2007). Facial expression recognition
in image sequences using geometric deformation fea-
tures and support vector machines. IEEE Transactions
on Image Processing, 16(1):172–187.
Lee, I.-M., Shiroma, E. J., Lobelo, F., Puska, P., Blair,
S. N., Katzmarzyk, P. T., Group, L. P. A. S. W.,
et al. (2012). Effect of physical inactivity on major
non-communicable diseases worldwide: an analysis
of burden of disease and life expectancy. The lancet,
380(9838):219–229.
Matos, N., Santos, A., and Vasconcelos, A. (2014). Kin-
teract: A multi-sensor physical rehabilitation solution
based on interactive games. In Communications in
Computer and Information Science.
Microsoft (2017). HighDetailFacePoints Enumera-
tion. https://msdn.microsoft.com/en-us/library/
microsoft.kinect.face.highdetailfacepoints.aspx,
[Online; accessed 2017-04-10].
Muhammad, P. and Devi, S. A. (2016). Hand gesture user
interface for smart devices based on mems sensors.
Procedia Computer Science, 93:940 – 946.
Mukhopadhyay, S. C. (2015). Wearable sensors for human
activity monitoring: A review. IEEE sensors journal,
15(3):1321–1330.
Murakami, K. and Taguchi, H. (1991). Gesture recognition
using recurrent neural networks. In Proceedings of the
SIGCHI conference on Human factors in computing
systems, pages 237–242. ACM.
Ohta, S., Nakamoto, H., Shinagawa, Y., and Tanikawa, T.
(2002). A health monitoring system for elderly peo-
ple living alone. Journal of telemedicine and telecare,
8(3):151–156.
Sch
¨
ondube, A., Kanning, M., and Fuchs, R. (2016).
The bidirectional effect between momentary affective
states and exercise duration on a day level. Frontiers
in Psychology, 7:1414.
Silva, C., Sobral, A., and Vieira, R. T. (2014). An auto-
matic facial expression recognition system evaluated
with different classifiers. X Workshop de Vis
˜
ao Com-
putacional (WVC’2014), pages 208–212.
Silva, V., Soares, F., Esteves, J. S., Figueiredo, J., Le
˜
ao,
C. P., Santos, C., and Paula, A. (2016). Real-
time Emotions Recognition System. In 8th Inter-
national Congress on Ultra Modern Telecommunica-
tions and Control Systems and Workshops (ICUMT),
pages 201–206, Lisboa.
Van Kasteren, T., Englebienne, G., and Kr
¨
ose, B. J. (2010).
An activity monitoring system for elderly care using
generative and discriminative models. Personal and
ubiquitous computing, 14(6):489–498.
Warburton, D. E., Nicol, C. W., and Bredin, S. S. (2006).
Health benefits of physical activity: the evidence.
Canadian medical association journal, 174(6):801–
809.
Wilde, A. G. (2010). An overview of human activity de-
tection technologies for pervasive systems. Depart-
ment of Informatics University of Fribourg, Switzer-
land, 212.
Youssef, A. E., Aly, S. F., Ibrahim, A. S., and Abbott,
a. L. (2013). Auto-Optimized Multimodal Expres-
sion Recognition Framework Using 3D Kinect Data
for ASD Therapeutic Aid. International Journal of
Modeling and Optimization, 3(2):112–115.
iBoccia - Monitoring Elderly While Playing Boccia Gameplay
675