Vision-Based Daily Routine Recognition for Healthcare with Transfer Learning
We propose to record Activities of Daily Living
(ADLs) of elderly people using a vision-based system so as to provide
better assistive and personalization technologies. Current ADL-related
research is based on data collected with help from non-elderly subjects
in laboratory environments and the activities performed are predetermined
for the sole purpose of data collection. To obtain more
realistic datasets for the application, we recorded ADLs for the elderly
with data collected from real-world environment involving real elderly
subjects. Motivated by the need to collect data for more effective
research related to elderly care, we chose to collect data in the room of
an elderly person. Specifically, we installed Kinect, a vision-based
sensor on the ceiling, to capture the activities that the elderly subject
performs in the morning every day. Based on the data, we identified
12 morning activities that the elderly person performs daily. To
recognize these activities, we created a HARELCARE framework to
investigate into the effectiveness of existing Human Activity
Recognition (HAR) algorithms and propose the use of a transfer
learning algorithm for HAR. We compared the performance, in terms
of accuracy, and training progress. Although the collected dataset is
relatively small, the proposed algorithm has a good potential to be
applied to all daily routine activities for healthcare purposes such as
evidence-based diagnosis and treatment.
[1] U. DESA, "World Population Prospects 2019: Highlights," New York
(US): United Nations Department for Economic and Social Affairs, 2019.
[2] J. Gill and M. J. Moore, "The State of aging & health in America 2013,"
2013. Available: https://www.statista.com/statistics/207347/causes-ofdeath-
among-us-adults-aged-65-by-ethnicity/.
[3] E. C. Nelson, T. Verhagen, and M. L. Noordzij, "Health empowerment
through activity trackers: An empirical smart wristband study,"
Computers in human behavior, vol. 62, pp. 364-374, 2016.
[4] E. Tak, R. Kuiper, A. Chorus, and M. Hopman-Rock, "Prevention of onset
and progression of basic ADL disability by physical activity in
community dwelling older adults: a meta-analysis," Ageing research
reviews, vol. 12, no. 1, pp. 329-338, 2013.
[5] M. Gabel, R. Gilad-Bachrach, E. Renshaw, and A. Schuster, "Full body
gait analysis with Kinect," in Engineering in Medicine and Biology
Society (EMBC), 2012 Annual International Conference of the IEEE,
2012: IEEE, pp. 1964-1967.
[6] Y. T. Liao, C.-L. Huang, and S.-C. Hsu, "Slip and fall event detection
using Bayesian Belief Network," Pattern recognition, vol. 45, no. 1, pp.
24-32, 2012.
[7] A. Elkholy, M. Hussein, W. Gomaa, D. Damen, and E. Saba, "Efficient
and Robust Skeleton-Based Quality Assessment and Abnormality
Detection in Human Action Performance," IEEE journal of biomedical
and health informatics, 2019.
[8] P. Lukowicz et al., "Recording a complex, multi modal activity data set
for context recognition," in Architecture of Computing Systems (ARCS),
2010 23rd International Conference on, 2010: VDE, pp. 1-6.
[9] K. Chapron, P. Lapointe, K. Bouchard, and S. Gaboury, "Highly Accurate
Bathroom Activity Recognition using Infrared Proximity Sensors," IEEE
Journal of Biomedical and Health Informatics, 2019.
[10] C. Chen, N. Kehtarnavaz, and R. Jafari, "A medication adherence
monitoring system for pill bottles based on a wearable inertial sensor," in
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual
International Conference of the IEEE, 2014: IEEE, pp. 4983-4986.
[11] G. Sprint, D. Cook, D. Weeks, J. Dahmen, and A. La Fleur, "Analyzing
sensor-based time series data to track changes in physical activity during
inpatient rehabilitation," Sensors, vol. 17, no. 10, p. 2219, 2017.
[12] Q. Ni, A. García Hernando, and I. de la Cruz, "The elderly’s independent
living in smart homes: A characterization of activities and sensing
infrastructure survey to facilitate services development," Sensors, vol. 15,
no. 5, pp. 11312-11362, 2015.
[13] F. Portet, M. Vacher, C. Golanski, C. Roux, and B. Meillon, "Design and
evaluation of a smart home voice interface for the elderly: acceptability
and objection aspects," Personal and Ubiquitous Computing, vol. 17, no.
1, pp. 127-144, 2013.
[14] L. Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Z. Yu, "Sensor-based
activity recognition," IEEE Transactions on Systems, Man, and
Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 790-
808, 2012.
[15] F. Palumbo, J. Ullberg, A. Štimec, F. Furfari, L. Karlsson, and S.
Coradeschi, "Sensor network infrastructure for a home care monitoring
system," Sensors, vol. 14, no. 3, pp. 3833-3860, 2014.
[16] H. Alemdar and C. Ersoy, "Wireless sensor networks for healthcare: A
survey," Computer networks, vol. 54, no. 15, pp. 2688-2710, 2010.
[17] J. K. Aggarwal and L. Xia, "Human activity recognition from 3d data: A
review," Pattern Recognition Letters, vol. 48, pp. 70-80, 2014.
[18] S. C. Mukhopadhyay, "Wearable sensors for human activity monitoring:
A review," IEEE sensors journal, vol. 15, no. 3, pp. 1321-1330, 2015.
[19] O. D. Lara and M. A. Labrador, "A survey on human activity recognition
using wearable sensors," IEEE Communications Surveys and Tutorials,
vol. 15, no. 3, pp. 1192-1209, 2013.
[20] A. Wickramasinghe, R. L. S. Torres, and D. C. Ranasinghe, "Recognition
of falls using dense sensing in an ambient assisted living environment,"
Pervasive and mobile computing, vol. 34, pp. 14-24, 2017.
[21] W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, "Understanding
and modeling of wifi signal based human activity recognition," in
Proceedings of the 21st annual international conference on mobile
computing and networking, 2015: ACM, pp. 65-76.
[22] H. Sagha et al., "Benchmarking classification techniques using the
Opportunity human activity dataset," in Systems, Man, and Cybernetics
(SMC), 2011 IEEE International Conference on, 2011: IEEE, pp. 36-40.
[23] F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, "Berkeley
MHAD: A comprehensive multimodal human action database," in
Applications of Computer Vision (WACV), 2013 IEEE Workshop on,
2013: IEEE, pp. 53-60.
[24] C. Chen, R. Jafari, and N. Kehtarnavaz, "A survey of depth and inertial
sensor fusion for human action recognition," Multimedia Tools and
Applications, vol. 76, no. 3, pp. 4405-4425, 2017.
[25] F. J. Ordóñez and D. Roggen, "Deep convolutional and lstm recurrent
neural networks for multimodal wearable activity recognition," Sensors,
vol. 16, no. 1, p. 115, 2016.
[26] J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Mining actionlet ensemble for
action recognition with depth cameras," in Computer Vision and Pattern
Recognition (CVPR), 2012 IEEE Conference on, 2012: IEEE, pp. 1290-
1297.
[27] C. Liu, Y. Hu, Y. Li, S. Song, and J. Liu, "PKU-MMD: A Large Scale
Benchmark for Skeleton-Based Human Action Understanding," in
Proceedings of the Workshop on Visual Analysis in Smart and Connected
Communities, 2017: ACM, pp. 1-8.
[28] J. Liu, A. Shahroudy, M. L. Perez, G. Wang, L.-Y. Duan, and A. K.
Chichung, "NTU RGB+ D 120: A Large-Scale Benchmark for 3D Human
Activity Understanding," IEEE transactions on pattern analysis and
machine intelligence, 2019.
[29] T. Van Kasteren, A. Noulas, G. Englebienne, and B. Kröse, "Accurate
activity recognition in a home setting," in Proceedings of the 10th
international conference on Ubiquitous computing, 2008: ACM, pp. 1-9.
[30] J. A. Stork, L. Spinello, J. Silva, and K. O. Arras, "Audio-based human
activity recognition using non-markovian ensemble voting," in RO-MAN,
2012 IEEE, 2012: IEEE, pp. 509-514.
[31] L. Guo, L. Wang, J. Liu, W. Zhou, and B. Lu, "HuAc: Human Activity
Recognition Using Crowdsourced WiFi Signals and Skeleton Data,"
Wireless Communications and Mobile Computing, vol. 2018, 2018.
[32] A. Reiss and D. Stricker, "Creating and benchmarking a new dataset for
physical activity monitoring," in Proceedings of the 5th International
Conference on PErvasive Technologies Related to Assistive
Environments, 2012: ACM, p. 40. [33] S. Katz, "Assessing self‐maintenance: activities of daily living, mobility,
and instrumental activities of daily living," Journal of the American
Geriatrics Society, vol. 31, no. 12, pp. 721-727, 1983.
[34] A. Jalal, S. Kamal, and D. Kim, "A depth video sensor-based life-logging
human activity recognition system for elderly care in smart indoor
environments," Sensors, vol. 14, no. 7, pp. 11735-11759, 2014.
[35] D. M. Gavrila and L. S. Davis, "Towards 3-d model-based tracking and
recognition of human movement: a multi-view approach," in International
workshop on automatic face-and gesture-recognition, 1995: Citeseer, pp.
272-277.
[36] N. Oliver, E. Horvitz, and A. Garg, "Layered representations for human
activity recognition," in Proceedings. Fourth IEEE International
Conference on Multimodal Interfaces, 2002: IEEE, pp. 3-8.
[37] R. Lublinerman, N. Ozay, D. Zarpalas, and O. Camps, "Activity
recognition from silhouettes using linear systems and model (in)
validation techniques," in 18th International Conference on Pattern
Recognition (ICPR'06), 2006, vol. 1: IEEE, pp. 347-350.
[38] J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, "Deep Learning for
Sensor-based Activity Recognition: A Survey," arXiv preprint
arXiv:1707.03502, 2017.
[39] "Kinect tools and resources." Available:
https://developer.microsoft.com/en-us/windows/kinect/tools.
[40] F. Lv and R. Nevatia, "Recognition and segmentation of 3-d human
action using hmm and multi-class adaboost," in European conference on
computer vision, 2006: Springer, pp. 359-372.
[41] J. Liu, A. Shahroudy, D. Xu, and G. Wang, "Spatio-temporal lstm with
trust gates for 3d human action recognition," in European Conference on
Computer Vision, 2016: Springer, pp. 816-833.
[42] S. Song, C. Lan, J. Xing, W. Zeng, and J. Liu, "An End-to-End Spatio-
Temporal Attention Model for Human Action Recognition from Skeleton
Data," in AAAI, 2017, pp. 4263-4270.
[43] J. Liu, G. Wang, P. Hu, L.-Y. Duan, and A. C. Kot, "Global contextaware
attention lstm networks for 3d action recognition," in CVPR, 2017.
[44] P. Zhang, C. Lan, J. Xing, W. Zeng, J. Xue, and N. Zheng, "View adaptive
recurrent neural networks for high performance human action recognition
from skeleton data," arXiv, no. Mar, 2017.
[45] M. Liu, H. Liu, and C. Chen, "Enhanced skeleton visualization for view
invariant human action recognition," Pattern Recognition, vol. 68, pp.
346-362, 2017.
[46] P. Wei, Y. Zhao, N. Zheng, and S.-C. Zhu, "Modeling 4D human-object
interactions for joint event segmentation, recognition, and object
localization," IEEE transactions on pattern analysis and machine
intelligence, vol. 39, no. 6, pp. 1165-1179, 2017.
[47] S. Althloothi, M. H. Mahoor, X. Zhang, and R. M. Voyles, "Human
activity recognition using multi-features and multiple kernel learning,"
Pattern recognition, vol. 47, no. 5, pp. 1800-1812, 2014.
[48] J. Liu, A. Shahroudy, D. Xu, A. K. Chichung, and G. Wang, "Skeleton-
Based Action Recognition Using Spatio-Temporal LSTM Network with
Trust Gates," IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2017.
[49] S. Yan, Y. Xiong, and D. Lin, "Spatial temporal graph convolutional
networks for skeleton-based action recognition," arXiv preprint
arXiv:1801.07455, 2018.
[50] J. Dean. "Building Intelligent Systems withLarge Scale Deep Learning."
Available: https://zh.scribd.com/document/355752799/Jeff-Dean-s-
Lecture-for-YC-AI.
[51] S. Ramasamy Ramamurthy and N. Roy, "Recent trends in machine
learning for human activity recognition—A survey," Wiley
Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.
8, no. 4, p. e1254, 2018.
[52] K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer
learning," Journal of Big data, vol. 3, no. 1, p. 9, 2016.
[53] S. V. Stehman, "Selecting and interpreting measures of thematic
classification accuracy," Remote sensing of Environment, vol. 62, no. 1,
pp. 77-89, 1997.
[54] G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction to
statistical learning. Springer, 2013.
[1] U. DESA, "World Population Prospects 2019: Highlights," New York
(US): United Nations Department for Economic and Social Affairs, 2019.
[2] J. Gill and M. J. Moore, "The State of aging & health in America 2013,"
2013. Available: https://www.statista.com/statistics/207347/causes-ofdeath-
among-us-adults-aged-65-by-ethnicity/.
[3] E. C. Nelson, T. Verhagen, and M. L. Noordzij, "Health empowerment
through activity trackers: An empirical smart wristband study,"
Computers in human behavior, vol. 62, pp. 364-374, 2016.
[4] E. Tak, R. Kuiper, A. Chorus, and M. Hopman-Rock, "Prevention of onset
and progression of basic ADL disability by physical activity in
community dwelling older adults: a meta-analysis," Ageing research
reviews, vol. 12, no. 1, pp. 329-338, 2013.
[5] M. Gabel, R. Gilad-Bachrach, E. Renshaw, and A. Schuster, "Full body
gait analysis with Kinect," in Engineering in Medicine and Biology
Society (EMBC), 2012 Annual International Conference of the IEEE,
2012: IEEE, pp. 1964-1967.
[6] Y. T. Liao, C.-L. Huang, and S.-C. Hsu, "Slip and fall event detection
using Bayesian Belief Network," Pattern recognition, vol. 45, no. 1, pp.
24-32, 2012.
[7] A. Elkholy, M. Hussein, W. Gomaa, D. Damen, and E. Saba, "Efficient
and Robust Skeleton-Based Quality Assessment and Abnormality
Detection in Human Action Performance," IEEE journal of biomedical
and health informatics, 2019.
[8] P. Lukowicz et al., "Recording a complex, multi modal activity data set
for context recognition," in Architecture of Computing Systems (ARCS),
2010 23rd International Conference on, 2010: VDE, pp. 1-6.
[9] K. Chapron, P. Lapointe, K. Bouchard, and S. Gaboury, "Highly Accurate
Bathroom Activity Recognition using Infrared Proximity Sensors," IEEE
Journal of Biomedical and Health Informatics, 2019.
[10] C. Chen, N. Kehtarnavaz, and R. Jafari, "A medication adherence
monitoring system for pill bottles based on a wearable inertial sensor," in
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual
International Conference of the IEEE, 2014: IEEE, pp. 4983-4986.
[11] G. Sprint, D. Cook, D. Weeks, J. Dahmen, and A. La Fleur, "Analyzing
sensor-based time series data to track changes in physical activity during
inpatient rehabilitation," Sensors, vol. 17, no. 10, p. 2219, 2017.
[12] Q. Ni, A. García Hernando, and I. de la Cruz, "The elderly’s independent
living in smart homes: A characterization of activities and sensing
infrastructure survey to facilitate services development," Sensors, vol. 15,
no. 5, pp. 11312-11362, 2015.
[13] F. Portet, M. Vacher, C. Golanski, C. Roux, and B. Meillon, "Design and
evaluation of a smart home voice interface for the elderly: acceptability
and objection aspects," Personal and Ubiquitous Computing, vol. 17, no.
1, pp. 127-144, 2013.
[14] L. Chen, J. Hoey, C. D. Nugent, D. J. Cook, and Z. Yu, "Sensor-based
activity recognition," IEEE Transactions on Systems, Man, and
Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 790-
808, 2012.
[15] F. Palumbo, J. Ullberg, A. Štimec, F. Furfari, L. Karlsson, and S.
Coradeschi, "Sensor network infrastructure for a home care monitoring
system," Sensors, vol. 14, no. 3, pp. 3833-3860, 2014.
[16] H. Alemdar and C. Ersoy, "Wireless sensor networks for healthcare: A
survey," Computer networks, vol. 54, no. 15, pp. 2688-2710, 2010.
[17] J. K. Aggarwal and L. Xia, "Human activity recognition from 3d data: A
review," Pattern Recognition Letters, vol. 48, pp. 70-80, 2014.
[18] S. C. Mukhopadhyay, "Wearable sensors for human activity monitoring:
A review," IEEE sensors journal, vol. 15, no. 3, pp. 1321-1330, 2015.
[19] O. D. Lara and M. A. Labrador, "A survey on human activity recognition
using wearable sensors," IEEE Communications Surveys and Tutorials,
vol. 15, no. 3, pp. 1192-1209, 2013.
[20] A. Wickramasinghe, R. L. S. Torres, and D. C. Ranasinghe, "Recognition
of falls using dense sensing in an ambient assisted living environment,"
Pervasive and mobile computing, vol. 34, pp. 14-24, 2017.
[21] W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, "Understanding
and modeling of wifi signal based human activity recognition," in
Proceedings of the 21st annual international conference on mobile
computing and networking, 2015: ACM, pp. 65-76.
[22] H. Sagha et al., "Benchmarking classification techniques using the
Opportunity human activity dataset," in Systems, Man, and Cybernetics
(SMC), 2011 IEEE International Conference on, 2011: IEEE, pp. 36-40.
[23] F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, "Berkeley
MHAD: A comprehensive multimodal human action database," in
Applications of Computer Vision (WACV), 2013 IEEE Workshop on,
2013: IEEE, pp. 53-60.
[24] C. Chen, R. Jafari, and N. Kehtarnavaz, "A survey of depth and inertial
sensor fusion for human action recognition," Multimedia Tools and
Applications, vol. 76, no. 3, pp. 4405-4425, 2017.
[25] F. J. Ordóñez and D. Roggen, "Deep convolutional and lstm recurrent
neural networks for multimodal wearable activity recognition," Sensors,
vol. 16, no. 1, p. 115, 2016.
[26] J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Mining actionlet ensemble for
action recognition with depth cameras," in Computer Vision and Pattern
Recognition (CVPR), 2012 IEEE Conference on, 2012: IEEE, pp. 1290-
1297.
[27] C. Liu, Y. Hu, Y. Li, S. Song, and J. Liu, "PKU-MMD: A Large Scale
Benchmark for Skeleton-Based Human Action Understanding," in
Proceedings of the Workshop on Visual Analysis in Smart and Connected
Communities, 2017: ACM, pp. 1-8.
[28] J. Liu, A. Shahroudy, M. L. Perez, G. Wang, L.-Y. Duan, and A. K.
Chichung, "NTU RGB+ D 120: A Large-Scale Benchmark for 3D Human
Activity Understanding," IEEE transactions on pattern analysis and
machine intelligence, 2019.
[29] T. Van Kasteren, A. Noulas, G. Englebienne, and B. Kröse, "Accurate
activity recognition in a home setting," in Proceedings of the 10th
international conference on Ubiquitous computing, 2008: ACM, pp. 1-9.
[30] J. A. Stork, L. Spinello, J. Silva, and K. O. Arras, "Audio-based human
activity recognition using non-markovian ensemble voting," in RO-MAN,
2012 IEEE, 2012: IEEE, pp. 509-514.
[31] L. Guo, L. Wang, J. Liu, W. Zhou, and B. Lu, "HuAc: Human Activity
Recognition Using Crowdsourced WiFi Signals and Skeleton Data,"
Wireless Communications and Mobile Computing, vol. 2018, 2018.
[32] A. Reiss and D. Stricker, "Creating and benchmarking a new dataset for
physical activity monitoring," in Proceedings of the 5th International
Conference on PErvasive Technologies Related to Assistive
Environments, 2012: ACM, p. 40. [33] S. Katz, "Assessing self‐maintenance: activities of daily living, mobility,
and instrumental activities of daily living," Journal of the American
Geriatrics Society, vol. 31, no. 12, pp. 721-727, 1983.
[34] A. Jalal, S. Kamal, and D. Kim, "A depth video sensor-based life-logging
human activity recognition system for elderly care in smart indoor
environments," Sensors, vol. 14, no. 7, pp. 11735-11759, 2014.
[35] D. M. Gavrila and L. S. Davis, "Towards 3-d model-based tracking and
recognition of human movement: a multi-view approach," in International
workshop on automatic face-and gesture-recognition, 1995: Citeseer, pp.
272-277.
[36] N. Oliver, E. Horvitz, and A. Garg, "Layered representations for human
activity recognition," in Proceedings. Fourth IEEE International
Conference on Multimodal Interfaces, 2002: IEEE, pp. 3-8.
[37] R. Lublinerman, N. Ozay, D. Zarpalas, and O. Camps, "Activity
recognition from silhouettes using linear systems and model (in)
validation techniques," in 18th International Conference on Pattern
Recognition (ICPR'06), 2006, vol. 1: IEEE, pp. 347-350.
[38] J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, "Deep Learning for
Sensor-based Activity Recognition: A Survey," arXiv preprint
arXiv:1707.03502, 2017.
[39] "Kinect tools and resources." Available:
https://developer.microsoft.com/en-us/windows/kinect/tools.
[40] F. Lv and R. Nevatia, "Recognition and segmentation of 3-d human
action using hmm and multi-class adaboost," in European conference on
computer vision, 2006: Springer, pp. 359-372.
[41] J. Liu, A. Shahroudy, D. Xu, and G. Wang, "Spatio-temporal lstm with
trust gates for 3d human action recognition," in European Conference on
Computer Vision, 2016: Springer, pp. 816-833.
[42] S. Song, C. Lan, J. Xing, W. Zeng, and J. Liu, "An End-to-End Spatio-
Temporal Attention Model for Human Action Recognition from Skeleton
Data," in AAAI, 2017, pp. 4263-4270.
[43] J. Liu, G. Wang, P. Hu, L.-Y. Duan, and A. C. Kot, "Global contextaware
attention lstm networks for 3d action recognition," in CVPR, 2017.
[44] P. Zhang, C. Lan, J. Xing, W. Zeng, J. Xue, and N. Zheng, "View adaptive
recurrent neural networks for high performance human action recognition
from skeleton data," arXiv, no. Mar, 2017.
[45] M. Liu, H. Liu, and C. Chen, "Enhanced skeleton visualization for view
invariant human action recognition," Pattern Recognition, vol. 68, pp.
346-362, 2017.
[46] P. Wei, Y. Zhao, N. Zheng, and S.-C. Zhu, "Modeling 4D human-object
interactions for joint event segmentation, recognition, and object
localization," IEEE transactions on pattern analysis and machine
intelligence, vol. 39, no. 6, pp. 1165-1179, 2017.
[47] S. Althloothi, M. H. Mahoor, X. Zhang, and R. M. Voyles, "Human
activity recognition using multi-features and multiple kernel learning,"
Pattern recognition, vol. 47, no. 5, pp. 1800-1812, 2014.
[48] J. Liu, A. Shahroudy, D. Xu, A. K. Chichung, and G. Wang, "Skeleton-
Based Action Recognition Using Spatio-Temporal LSTM Network with
Trust Gates," IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2017.
[49] S. Yan, Y. Xiong, and D. Lin, "Spatial temporal graph convolutional
networks for skeleton-based action recognition," arXiv preprint
arXiv:1801.07455, 2018.
[50] J. Dean. "Building Intelligent Systems withLarge Scale Deep Learning."
Available: https://zh.scribd.com/document/355752799/Jeff-Dean-s-
Lecture-for-YC-AI.
[51] S. Ramasamy Ramamurthy and N. Roy, "Recent trends in machine
learning for human activity recognition—A survey," Wiley
Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.
8, no. 4, p. e1254, 2018.
[52] K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer
learning," Journal of Big data, vol. 3, no. 1, p. 9, 2016.
[53] S. V. Stehman, "Selecting and interpreting measures of thematic
classification accuracy," Remote sensing of Environment, vol. 62, no. 1,
pp. 77-89, 1997.
[54] G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction to
statistical learning. Springer, 2013.
@article{"International Journal of Medical, Medicine and Health Sciences:79718", author = "Bruce X. B. Yu and Yan Liu and Keith C. C. Chan", title = "Vision-Based Daily Routine Recognition for Healthcare with Transfer Learning", abstract = "We propose to record Activities of Daily Living
(ADLs) of elderly people using a vision-based system so as to provide
better assistive and personalization technologies. Current ADL-related
research is based on data collected with help from non-elderly subjects
in laboratory environments and the activities performed are predetermined
for the sole purpose of data collection. To obtain more
realistic datasets for the application, we recorded ADLs for the elderly
with data collected from real-world environment involving real elderly
subjects. Motivated by the need to collect data for more effective
research related to elderly care, we chose to collect data in the room of
an elderly person. Specifically, we installed Kinect, a vision-based
sensor on the ceiling, to capture the activities that the elderly subject
performs in the morning every day. Based on the data, we identified
12 morning activities that the elderly person performs daily. To
recognize these activities, we created a HARELCARE framework to
investigate into the effectiveness of existing Human Activity
Recognition (HAR) algorithms and propose the use of a transfer
learning algorithm for HAR. We compared the performance, in terms
of accuracy, and training progress. Although the collected dataset is
relatively small, the proposed algorithm has a good potential to be
applied to all daily routine activities for healthcare purposes such as
evidence-based diagnosis and treatment.", keywords = "Daily activity recognition, healthcare, IoT sensors,
transfer learning.", volume = "14", number = "7", pages = "190-9", }
