Total views : 240

Identifying the Gestures of Toddler, Pregnant Woman and Elderly using Segmented Pigeon Hole Feature Extraction Technique and IR-Threshold Classifier


  • School of Computing Science and Engineering, VIT University, Chennai- 632014, Tamil Nadu, India


Objectives: The Objective of this research is to develop a feature extractor and a classifier which will identify and classify the gestures of infants, elderly and pregnant woman using Gait Signal re-ceived from wearable electrodes which is positioned on the body of subjects. Methods/Statistical Analysis: Remote health care monitoring is a technology which enables monitoring a person outside usual medical settings i.e., in the house or residence, which may increase access to caretakers or person at home but it will decrease healthcare deliverance costs. Findings: A novel segmented pigeon hole data extraction and reduction technique is proposed for reducing data and feature extraction. Secondly an Iteration Reduced Threshold based Classifier (IR-Threshold Classifier) has been introduced, which classifies the reduced extracted data into Safe and Danger for toddler, Normal and contra for pregnant women and Stable and Fall for elderly. Feature extraction and reduction using Segmented Pigeon Hole algorithm reduced the dataset for this domain. It is compared with bench mark data set and it had produced the significant data reduction. The IR Threshold classifier had shown 95% of accuracy when compared with the other classifiers. Applications/Improvements: This gives the best predominant electrode set by reducing data which will increase the classification accuracy.


Fall and Normal Category, Feature Extraction, IR-Threshold Classifier, Machine Learning Algorithm, Segmented Pigeon Hole, Wearable Electrodes.

Full Text:

 |  (PDF views: 187)


  • Roweis ST, Saul LK. Nonlinear dimen-sionality reduction by locally linear embed-ding. Science. 2000; 290(5500): 2323–26.
  • Shao L, Seed L, Mubashir M. A survey on fall detection: Principles and ap-proaches. Neuro Computing. 2013; 100:144–52.
  • Li B, Wang J, Simon Sherratt R, Lee S, Zhang Z. Enhanced fall detection system for elderly person monitoring using con-sumer home networks. IEEE Transactions on Consumer Electronics. 2014; 60(1):23–9.
  • Naqvi SM, Rhuma A, Yu M, Wang L. A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Transac-tions on Information Technology in Bio-medicine. 2012; 16(6):1274–86.
  • Ho KC, Popescu M, Li Y. A microphone array system for automatic fall detection. IEEE Transactions on Biomedical Engi-neering. 2012; 59(2):1291–301.
  • Li Y, Rantz M, Popescu M, Skubic M. An acoustic fall detector system that uses sound height information to reduce the false alarm rate. Proc 30th Int IEEE Eng in Medicine and Biol Soc Conference; 2008. p. 4628–31.
  • Jiang P, Jiang W, Winkley J. Verity: An ambient assisted living platform. IEEE Transactions on Consumer Electronics. 2012; 58(2):364–73.
  • Vaidehi K, Subashini TS. Breast tissue characterization using combined K-NN Classifier. Indian Journal of Science and Technology. 2015 Jan; 8(1). DOI: 10.17485/ijst/2015/v8i1/52818.
  • Jayasri T, Hemalatha M. Categorization of respiratory signal using ANN and SVM based on Feature Extraction Algorithm. In-dian Journal of Science and Technology. 2013 Sep; 6(9). DOI: 10.17485/ijst/2013/v6i9/37144.
  • Oliver AS, Samraj A, Maheswari N. Fuzzy ARTMAP based classification feature for danger and safety zone pre-diction for toddlers using wearable elec-trodes. Proceeding of IEEE Internaional Conference on High Performance Green Computing. ICGHPC’13; 2013. p. 1–6.
  • Oliver AS, Maheswari, Andrews S. Stable and critical gesture recognition in children and pregnant women by SVM classification with FFT features of signals from wearable attires. Research Journal of Applied Sciences, Engineering and Tech-nology. 2014; 7(23):4917–26.
  • Rhuma A, Yu M, Naqvi SMR, Wang L. An online one class support vector ma-chine-based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE Journal of Biomedical and Health Informatics. 2013; 17(6):1002–14.
  • Xuan D, Dai J, Bai X, Shen Z, Yang Z. Mobile phone-based pervasive fall detec-tion. Journal of Personal Ubiquots Com-putting. 2010; 14(7):633–43.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.