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Smartphone-sensors Based Activity Recognition Using IndRNN

Conference Paper


Abstract


  • Human activity recognition based on the smartphone sensors has the potential to impact a wide range of applications such as healthcare, smart home, and remote monitoring. For simple activities like “Sit” and “Walk”, it can be distinguished relatively easily. However, for similar activities with respect to transportations, such as “Train”, “Bus” and “Subway”, it still remains an open problem. To tackle this problem, this paper uses the recently proposed Independently Recurrent Neural Network (IndRNN) to process data of different lengths in order to capture the temporal patterns at different granularities. The unique property of IndRNN in terms of the gradient propagation allows it to construct deep RNN networks and extract good features. The proposed method has been applied and submitted to SHL recognition challenge as “UOW AMRL”.

UOW Authors


  •   Li, Shuai (external author)
  •   Li, Chuankun (external author)
  •   Li, Wanqing
  •   Hou, Yonghong (external author)
  •   Cook, Christopher

Publication Date


  • 2018

Citation


  • Li, S., Li, C., Li, W., Hou, Y. & Cook, C. (2018). Smartphone-sensors Based Activity Recognition Using IndRNN. UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers (pp. 1541-1547). New York, United States: ACM.

Scopus Eid


  • 2-s2.0-85058296996

Start Page


  • 1541

End Page


  • 1547

Place Of Publication


  • New York, United States

Abstract


  • Human activity recognition based on the smartphone sensors has the potential to impact a wide range of applications such as healthcare, smart home, and remote monitoring. For simple activities like “Sit” and “Walk”, it can be distinguished relatively easily. However, for similar activities with respect to transportations, such as “Train”, “Bus” and “Subway”, it still remains an open problem. To tackle this problem, this paper uses the recently proposed Independently Recurrent Neural Network (IndRNN) to process data of different lengths in order to capture the temporal patterns at different granularities. The unique property of IndRNN in terms of the gradient propagation allows it to construct deep RNN networks and extract good features. The proposed method has been applied and submitted to SHL recognition challenge as “UOW AMRL”.

UOW Authors


  •   Li, Shuai (external author)
  •   Li, Chuankun (external author)
  •   Li, Wanqing
  •   Hou, Yonghong (external author)
  •   Cook, Christopher

Publication Date


  • 2018

Citation


  • Li, S., Li, C., Li, W., Hou, Y. & Cook, C. (2018). Smartphone-sensors Based Activity Recognition Using IndRNN. UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers (pp. 1541-1547). New York, United States: ACM.

Scopus Eid


  • 2-s2.0-85058296996

Start Page


  • 1541

End Page


  • 1547

Place Of Publication


  • New York, United States