Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments
With the widespread adoption of the Internet-connected
devices, and with the prevalence of the Internet of Things (IoT)
applications, there is an increased interest in machine learning
techniques that can provide useful and interesting services in the
smart home domain. The areas that machine learning techniques
can help advance are varied and ever-evolving. Classifying smart
home inhabitants’ Activities of Daily Living (ADLs), is one
prominent example. The ability of machine learning technique to find
meaningful spatio-temporal relations of high-dimensional data is an
important requirement as well. This paper presents a comparative
evaluation of state-of-the-art machine learning techniques to classify
ADLs in the smart home domain. Forty-two synthetic datasets and
two real-world datasets with multiple inhabitants are used to evaluate
and compare the performance of the identified machine learning
techniques. Our results show significant performance differences
between the evaluated techniques. Such as AdaBoost, Cortical
Learning Algorithm (CLA), Decision Trees, Hidden Markov Model
(HMM), Multi-layer Perceptron (MLP), Structured Perceptron and
Support Vector Machines (SVM). Overall, neural network based
techniques have shown superiority over the other tested techniques.
[1] L. Bartram, J. Rodgers, and R. Woodbury, “Smart homes or smart
occupants? supporting aware living in the home,” Human-Computer
Interaction–INTERACT 2011, pp. 52–64, 2011.
[2] B. Minor, J. R. Doppa, and D. J. Cook, “Data-driven activity prediction:
Algorithms, evaluation methodology, and applications,” in Proceedings
of the 21th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. ACM, 2015, pp. 805–814.
[3] I. Fatima, M. Fahim, Y.-K. Lee, and S. Lee, “Effects of smart home
dataset characteristics on classifiers performance for human activity
recognition,” in Computer Science and its Applications. Springer, 2012,
pp. 271–281.
[4] E. M. Tapia, S. S. Intille, and K. Larson, “Activity recognition in
the home using simple and ubiquitous sensors,” in Pervasive, vol. 4.
Springer, 2004, pp. 158–175.
[5] E. Thomaz, T. Pl¨otz, I. Essa, and G. D. Abowd, “Interactive techniques
for labeling activities of daily living to assist machine learning,” in
Proceedings of Workshop on Interactive Systems in Healthcare, 2011.
[6] S. T. M. Bourobou and Y. Yoo, “User activity recognition in smart homes
using pattern clustering applied to temporal ann algorithm,” Sensors,
vol. 15, no. 5, pp. 11 953–11 971, 2015.
[7] D. J. Cook, M. Youngblood, E. O. Heierman, K. Gopalratnam, S. Rao,
A. Litvin, and F. Khawaja, “Mavhome: An agent-based smart home,”
in Pervasive Computing and Communications, 2003.(PerCom 2003).
Proceedings of the First IEEE International Conference on. IEEE,
2003, pp. 521–524.
[8] C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning,
vol. 20, no. 3, pp. 273–297, 1995.
[9] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion,
O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg,
J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and
E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of
Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [10] A. Fleury, M. Vacher, and N. Noury, “Svm-based multimodal
classification of activities of daily living in health smart homes:
sensors, algorithms, and first experimental results,” IEEE transactions
on information technology in biomedicine, vol. 14, no. 2, pp. 274–283,
2010.
[11] L. E. Baum and T. Petrie, “Statistical inference for probabilistic
functions of finite state markov chains,” The annals of mathematical
statistics, vol. 37, no. 6, pp. 1554–1563, 1966.
[12] H. Alemdar, H. Ertan, O. D. Incel, and C. Ersoy, “Aras human
activity datasets in multiple homes with multiple residents,” in
Proceedings of the 7th International Conference on Pervasive
Computing Technologies for Healthcare. ICST (Institute for Computer
Sciences, Social-Informatics and Telecommunications Engineering),
2013, pp. 232–235.
[13] M. Prossegger and A. Bouchachia, “Multi-resident activity recognition
using incremental decision trees,” in Adaptive and Intelligent Systems.
Springer, 2014, pp. 182–191.
[14] G. Manogaran and D. Lopez, “Health data analytics using scalable
logistic regression with stochastic gradient descent,” International
Journal of Advanced Intelligence Paradigms, vol. 8, no. 2, 2017.
[15] Y. Freund and R. E. Schapire, “A desicion-theoretic generalization of
on-line learning and an application to boosting,” in European conference
on computational learning theory. Springer, 1995, pp. 23–37.
[16] B. Logan and J. Healey, “Sensors to detect the activities of daily living,”
in Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th
Annual International Conference of the IEEE. IEEE, 2006, pp.
5362–5365.
[17] J. Hawkins and S. Ahmad, “Why neurons have thousands of
synapses, a theory of sequence memory in neocortex,” Frontiers
in Neural Circuits, vol. 10, p. 23, 2016. [Online]. Available:
https://www.frontiersin.org/article/10.3389/fncir.2016.00023.
[18] J. Hawkins, S. Ahmad, S. Purdy, and A. Lavin, “Biological and
machine intelligence (bami),” 2016, initial online release 0.4. (Online).
Available: http://numenta.com/biological-and-machine-intelligence/.
[19] R. ˇSkoviera and I. Bajla, “Image classification based on hierarchical
temporal memory and color features,” Slovak Academy of Sciences,
MEASUREMENT, 2013.
[20] I. Arel, D. C. Rose, and T. P. Karnowski, “Deep machine learning-a
new frontier in artificial intelligence research (research frontier),” IEEE
computational intelligence magazine, vol. 5, no. 4, pp. 13–18, 2010.
[21] F. D. S. Webber, “Semantic folding theory and its application in semantic
fingerprinting,” arXiv preprint arXiv:1511.08855, 2015.
[22] M. Otahal and O. Stepankova, “Anomaly detection with cortical learning
algorithm for smart homes,” SMART HOMES, vol. 20144, p. 24.
[23] F. Rosenblatt, “Principles of neurodynamics. perceptrons and the theory
of brain mechanisms,” Cornell Aeronautical Lab Inc Buffalo NY, Tech.
Rep., 1961.
[24] M. Collins, “Discriminative training methods for hidden markov models:
Theory and experiments with perceptron algorithms,” in Proceedings
of the ACL-02 conference on Empirical methods in natural language
processing-Volume 10. Association for Computational Linguistics,
2002, pp. 1–8.
[25] L. Zhu, Y. Chen, X. Ye, and A. Yuille, “Structure-perceptron learning
of a hierarchical log-linear model,” in Computer Vision and Pattern
Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008,
pp. 1–8.
[26] H. Fang and L. He, “Bp neural network for human activity recognition
in smart home,” in Computer Science & Service System (CSSS), 2012
International Conference on. IEEE, 2012, pp. 1034–1037.
[27] Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies
with gradient descent is difficult,” IEEE transactions on neural networks,
vol. 5, no. 2, pp. 157–166, 1994.
[28] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural
computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[29] F. J. Ord´o˜nez and D. Roggen, “Deep convolutional and lstm recurrent
neural networks for multimodal wearable activity recognition,” Sensors,
vol. 16, no. 1, p. 115, 2016.
[30] D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. F¨orster, G. Tr¨oster,
P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha et al., “Collecting
complex activity datasets in highly rich networked sensor environments,”
in Networked Sensing Systems (INSS), 2010 Seventh International
Conference on. IEEE, 2010, pp. 233–240.
[31] P. Zappi, C. Lombriser, T. Stiefmeier, E. Farella, D. Roggen,
L. Benini, and G. Troster, “Activity recognition from on-body sensors:
accuracy-power trade-off by dynamic sensor selection,” Lecture Notes
in Computer Science, vol. 4913, p. 17, 2008.
[32] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning
applied to document recognition,” Proceedings of the IEEE, vol. 86,
no. 11, pp. 2278–2324, 1998.
[33] M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and
J. Zhang, “Convolutional neural networks for human activity recognition
using mobile sensors,” in Mobile Computing, Applications and Services
(MobiCASE), 2014 6th International Conference on. IEEE, 2014, pp.
197–205.
[34] N. Alshammari, T. Alshammari, M. Sedky, J. Champion, and C. Bauer,
“Openshs: Open smart home simulator,” Sensors, vol. 17, no. 5, 2017.
[35] D. Cook, M. Schmitter-Edgecombe, A. Crandall, C. Sanders, and
B. Thomas, “Collecting and disseminating smart home sensor data in
the casas project,” in Proceedings of the CHI Workshop on Developing
Shared Home Behavior Datasets to Advance HCI and Ubiquitous
Computing Research, 2009, pp. 1–7.
[36] K. Bouchard, A. Ajroud, B. Bouchard, and A. Bouzouane, “Simact: a 3d
open source smart home simulator for activity recognition,” Advances
in Computer Science and Information Technology, pp. 524–533, 2010.
[37] J. Synnott, C. Nugent, and P. Jeffers, “Simulation of smart home activity
datasets,” Sensors, vol. 15, no. 6, pp. 14 162–14 179, 2015.
[38] F. Chollet et al., “Keras,” https://github.com/fchollet/keras, 2015.
[39] K. Gopalratnam and D. J. Cook, “Active lezi: An incremental parsing
algorithm for sequential prediction,” International Journal on Artificial
Intelligence Tools, vol. 13, no. 04, pp. 917–929, 2004.
[1] L. Bartram, J. Rodgers, and R. Woodbury, “Smart homes or smart
occupants? supporting aware living in the home,” Human-Computer
Interaction–INTERACT 2011, pp. 52–64, 2011.
[2] B. Minor, J. R. Doppa, and D. J. Cook, “Data-driven activity prediction:
Algorithms, evaluation methodology, and applications,” in Proceedings
of the 21th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining. ACM, 2015, pp. 805–814.
[3] I. Fatima, M. Fahim, Y.-K. Lee, and S. Lee, “Effects of smart home
dataset characteristics on classifiers performance for human activity
recognition,” in Computer Science and its Applications. Springer, 2012,
pp. 271–281.
[4] E. M. Tapia, S. S. Intille, and K. Larson, “Activity recognition in
the home using simple and ubiquitous sensors,” in Pervasive, vol. 4.
Springer, 2004, pp. 158–175.
[5] E. Thomaz, T. Pl¨otz, I. Essa, and G. D. Abowd, “Interactive techniques
for labeling activities of daily living to assist machine learning,” in
Proceedings of Workshop on Interactive Systems in Healthcare, 2011.
[6] S. T. M. Bourobou and Y. Yoo, “User activity recognition in smart homes
using pattern clustering applied to temporal ann algorithm,” Sensors,
vol. 15, no. 5, pp. 11 953–11 971, 2015.
[7] D. J. Cook, M. Youngblood, E. O. Heierman, K. Gopalratnam, S. Rao,
A. Litvin, and F. Khawaja, “Mavhome: An agent-based smart home,”
in Pervasive Computing and Communications, 2003.(PerCom 2003).
Proceedings of the First IEEE International Conference on. IEEE,
2003, pp. 521–524.
[8] C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning,
vol. 20, no. 3, pp. 273–297, 1995.
[9] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion,
O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg,
J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and
E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of
Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [10] A. Fleury, M. Vacher, and N. Noury, “Svm-based multimodal
classification of activities of daily living in health smart homes:
sensors, algorithms, and first experimental results,” IEEE transactions
on information technology in biomedicine, vol. 14, no. 2, pp. 274–283,
2010.
[11] L. E. Baum and T. Petrie, “Statistical inference for probabilistic
functions of finite state markov chains,” The annals of mathematical
statistics, vol. 37, no. 6, pp. 1554–1563, 1966.
[12] H. Alemdar, H. Ertan, O. D. Incel, and C. Ersoy, “Aras human
activity datasets in multiple homes with multiple residents,” in
Proceedings of the 7th International Conference on Pervasive
Computing Technologies for Healthcare. ICST (Institute for Computer
Sciences, Social-Informatics and Telecommunications Engineering),
2013, pp. 232–235.
[13] M. Prossegger and A. Bouchachia, “Multi-resident activity recognition
using incremental decision trees,” in Adaptive and Intelligent Systems.
Springer, 2014, pp. 182–191.
[14] G. Manogaran and D. Lopez, “Health data analytics using scalable
logistic regression with stochastic gradient descent,” International
Journal of Advanced Intelligence Paradigms, vol. 8, no. 2, 2017.
[15] Y. Freund and R. E. Schapire, “A desicion-theoretic generalization of
on-line learning and an application to boosting,” in European conference
on computational learning theory. Springer, 1995, pp. 23–37.
[16] B. Logan and J. Healey, “Sensors to detect the activities of daily living,”
in Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th
Annual International Conference of the IEEE. IEEE, 2006, pp.
5362–5365.
[17] J. Hawkins and S. Ahmad, “Why neurons have thousands of
synapses, a theory of sequence memory in neocortex,” Frontiers
in Neural Circuits, vol. 10, p. 23, 2016. [Online]. Available:
https://www.frontiersin.org/article/10.3389/fncir.2016.00023.
[18] J. Hawkins, S. Ahmad, S. Purdy, and A. Lavin, “Biological and
machine intelligence (bami),” 2016, initial online release 0.4. (Online).
Available: http://numenta.com/biological-and-machine-intelligence/.
[19] R. ˇSkoviera and I. Bajla, “Image classification based on hierarchical
temporal memory and color features,” Slovak Academy of Sciences,
MEASUREMENT, 2013.
[20] I. Arel, D. C. Rose, and T. P. Karnowski, “Deep machine learning-a
new frontier in artificial intelligence research (research frontier),” IEEE
computational intelligence magazine, vol. 5, no. 4, pp. 13–18, 2010.
[21] F. D. S. Webber, “Semantic folding theory and its application in semantic
fingerprinting,” arXiv preprint arXiv:1511.08855, 2015.
[22] M. Otahal and O. Stepankova, “Anomaly detection with cortical learning
algorithm for smart homes,” SMART HOMES, vol. 20144, p. 24.
[23] F. Rosenblatt, “Principles of neurodynamics. perceptrons and the theory
of brain mechanisms,” Cornell Aeronautical Lab Inc Buffalo NY, Tech.
Rep., 1961.
[24] M. Collins, “Discriminative training methods for hidden markov models:
Theory and experiments with perceptron algorithms,” in Proceedings
of the ACL-02 conference on Empirical methods in natural language
processing-Volume 10. Association for Computational Linguistics,
2002, pp. 1–8.
[25] L. Zhu, Y. Chen, X. Ye, and A. Yuille, “Structure-perceptron learning
of a hierarchical log-linear model,” in Computer Vision and Pattern
Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008,
pp. 1–8.
[26] H. Fang and L. He, “Bp neural network for human activity recognition
in smart home,” in Computer Science & Service System (CSSS), 2012
International Conference on. IEEE, 2012, pp. 1034–1037.
[27] Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies
with gradient descent is difficult,” IEEE transactions on neural networks,
vol. 5, no. 2, pp. 157–166, 1994.
[28] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural
computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[29] F. J. Ord´o˜nez and D. Roggen, “Deep convolutional and lstm recurrent
neural networks for multimodal wearable activity recognition,” Sensors,
vol. 16, no. 1, p. 115, 2016.
[30] D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. F¨orster, G. Tr¨oster,
P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha et al., “Collecting
complex activity datasets in highly rich networked sensor environments,”
in Networked Sensing Systems (INSS), 2010 Seventh International
Conference on. IEEE, 2010, pp. 233–240.
[31] P. Zappi, C. Lombriser, T. Stiefmeier, E. Farella, D. Roggen,
L. Benini, and G. Troster, “Activity recognition from on-body sensors:
accuracy-power trade-off by dynamic sensor selection,” Lecture Notes
in Computer Science, vol. 4913, p. 17, 2008.
[32] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning
applied to document recognition,” Proceedings of the IEEE, vol. 86,
no. 11, pp. 2278–2324, 1998.
[33] M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu, and
J. Zhang, “Convolutional neural networks for human activity recognition
using mobile sensors,” in Mobile Computing, Applications and Services
(MobiCASE), 2014 6th International Conference on. IEEE, 2014, pp.
197–205.
[34] N. Alshammari, T. Alshammari, M. Sedky, J. Champion, and C. Bauer,
“Openshs: Open smart home simulator,” Sensors, vol. 17, no. 5, 2017.
[35] D. Cook, M. Schmitter-Edgecombe, A. Crandall, C. Sanders, and
B. Thomas, “Collecting and disseminating smart home sensor data in
the casas project,” in Proceedings of the CHI Workshop on Developing
Shared Home Behavior Datasets to Advance HCI and Ubiquitous
Computing Research, 2009, pp. 1–7.
[36] K. Bouchard, A. Ajroud, B. Bouchard, and A. Bouzouane, “Simact: a 3d
open source smart home simulator for activity recognition,” Advances
in Computer Science and Information Technology, pp. 524–533, 2010.
[37] J. Synnott, C. Nugent, and P. Jeffers, “Simulation of smart home activity
datasets,” Sensors, vol. 15, no. 6, pp. 14 162–14 179, 2015.
[38] F. Chollet et al., “Keras,” https://github.com/fchollet/keras, 2015.
[39] K. Gopalratnam and D. J. Cook, “Active lezi: An incremental parsing
algorithm for sequential prediction,” International Journal on Artificial
Intelligence Tools, vol. 13, no. 04, pp. 917–929, 2004.
@article{"International Journal of Information, Control and Computer Sciences:76955", author = "Talal Alshammari and Nasser Alshammari and Mohamed Sedky and Chris Howard", title = "Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments", abstract = "With the widespread adoption of the Internet-connected
devices, and with the prevalence of the Internet of Things (IoT)
applications, there is an increased interest in machine learning
techniques that can provide useful and interesting services in the
smart home domain. The areas that machine learning techniques
can help advance are varied and ever-evolving. Classifying smart
home inhabitants’ Activities of Daily Living (ADLs), is one
prominent example. The ability of machine learning technique to find
meaningful spatio-temporal relations of high-dimensional data is an
important requirement as well. This paper presents a comparative
evaluation of state-of-the-art machine learning techniques to classify
ADLs in the smart home domain. Forty-two synthetic datasets and
two real-world datasets with multiple inhabitants are used to evaluate
and compare the performance of the identified machine learning
techniques. Our results show significant performance differences
between the evaluated techniques. Such as AdaBoost, Cortical
Learning Algorithm (CLA), Decision Trees, Hidden Markov Model
(HMM), Multi-layer Perceptron (MLP), Structured Perceptron and
Support Vector Machines (SVM). Overall, neural network based
techniques have shown superiority over the other tested techniques.", keywords = "Activities of daily living, classification, internet of
things, machine learning, smart home.", volume = "12", number = "2", pages = "72-7", }
