Abstract
Despite various Inertial Measurement Unit (IMU) applications and their technological advances, the process of motion segmentation performed by accelerometers continues to be essential for finding the points at which motion starts and ends. In contrast to the fast growing and diverse requirements of IMU applications for motion segmentation, the evaluation of its accuracy is in need of improvement. Accuracy-oriented evaluation is unable to directly indicate motion discontinuity in the estimated results, and present enough information satisfying various requirements. To complement conventional evaluation methods, we propose a multidimensional evaluation based on new additional evaluation criteria, and justify their availability by assessing nine conventional algorithms. Through an experiment based on 462 handwriting measurements from 19 subjects, we show that algorithms with high accuracy are sensitive to movements that are unintentional and fine, but are unable to specify unexpected motion partitioning. On the other hand, we verify that our proposed metrics describe the status of both energy smoothness and parameter tuning for the motion discontinuity suppression. It also appears that a minimum time delay of 150 ms is required to reliably suppress the motion discontinuity, and algorithms with longer time delay do not always assure sufficient motion discontinuity suppression. Additionally, axial information integration performed only after securing reliable energy smoothness along each axis can guarantee significant performance improvements. As a result, it turns out that a key factor for reliable motion segmentation is how to generate well smoothed energy with minimum time delay, and the deliberate selection of algorithms with smoother energy and less time delay is a better strategy than the delicate parameter adjustment in a fixed algorithm with poorer energy status under the same condition. Using the analysis based on the proposed criteria, the selection of motion segmentation and the adjustment of the parameters for a given purpose are finally introduced as an application of the proposed multidimensional evaluation.
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References
Allen R, Moore R, Whelan M (1996) Application of neural networks to plasma etch end point detection. J Vac Sci Technol B 14(1): 498-503.
Baek J, Lee G, Park W, Yun BJ (2004) Accelerometer signal processing for user activity detection. In: Negoita MGh, Howlett RJ, Jain LC (eds) Knowledge-Based Intelligent Information and Engineering Systems. 8th International Conference, KES 2004, Wellington, New Zealand, September 20–25, 2004, Proceedings, Part III. Lecture Notes in Computer Science, vol 3215, Springer Berlin Heidelberg, pp 610–617
Bang WC, Chang W, Kang KH, Choi ES, Potanin A, Kim DY “Self-contained spatial input device for wearable computers.” pp. 26–34.
Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Ferscha A, Mattern F (eds) Pervasive Computing. Second International Conference, PERVASIVE 2004, Linz/Vienna, Austria, April 21–23, 2004. Proceedings. Lecture Notes in Computer Science, vol. 3001, Springer Berlin Heidelberg, pp. 1–17
Barger T, Brown D, Alwan M (2005) Health-status monitoring through analysis of behavioral patterns. IEEE Trans Syst Man Cybern Part A Syst Humans 35(1):22–27
Baron R, Plamondon R (1989) Acceleration measurement with an instrumented Pen for signature verification and handwriting analysis. IEEE Trans Instrum Meas 38(6):1132–1138
Benbasat AY, Paradiso JA (2002) An inertial measurement framework for gesture recognition and applications. In: Wachsmuth I, Sowa T (eds) Gesture and Sign Language in Human-Computer Interaction. International Gesture Workshop, GW 2001 London, UK, April 18–20, 2001 Revised Papers. Lecture Notes in Computer Science, vol. 2298, Springer Berlin Heidelberg, pp. 9–20
Borza PV (2008) Motion-based Gesture Recognition with an Accelerometer. Mathematics and Computer Science, Babes-Bolyai University of Cluj-NapocaMaster’s thesis, Babes-Bolyai University, Faculty of Mathematics and Computer Science. https://accelges.googlecode.com/files/ThesisPaper.pdf
Bulling A, Blanke U, Schiele B (2014) “A tutorial on human activity recognition using body-worn inertial sensors,”. ACM Comput Surv (CSUR) 46(3):33
Choi ES, Bang WC, Cho SJ, Yang J, Kim DY, Kim SR (2005) Beatbox music phone: gesture-based interactive mobile phone using a tri-axis accelerometer. In: International Conference on Information Technology - ICIT, 2005, Hong Kong, 14–17 December 2005
Choi SD, Lee AS, Lee SY (2006) On-Line Handwritten Character Recognition with 3D Accelerometer. In: Proc. IEEE Int Conf Inf Acquisition, pp. 845–850
Choi SD, Lee SY (2012) 3D stroke reconstruction and cursive script recognition with magnetometer-aided inertial measurement unit. Consum Electron IEEE Trans 58(2):661–669
Deng Y, He G, Kuppusamy P, Zweier JL (2003) “Deconvolution algorithm based on automatic cutoff frequency selection for EPR imaging,”. Magn Reson Med 50(2):5
DeVaul RW, Dunn S (2001) Real-time motion classification for wearable computing applications. 2001 Project Paper
Dong Z, Zhang G, Luo Y, Tsang CC, Shi G, Kwok SY, Li WJ, Leong PH, Wong MY (2007) A calibration method for MEMS inertial sensors based on optical tracking. In: Proceedings of the second IEEE international conference on nano/micro engineered and molecular systems, Bangkok, Thailand, 16–19 January 2007
Duda RO, Hart PE, Stork DG (2012) Pattern classification: Wiley-interscience
Flash T, Hogan N (1985) The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 5(7):1688–1703
Gao KL, Tsang WWN (2008) Use of accelerometry to quantify the physical activity level of the elderly. Hong Kong Physiother J 26:18–23
Ge X, Smyth P (2001) Segmental Semi-Markov models for endpoint detection in plasma etching. IEEE Trans Semicond Eng
Godfrey A, Conway R, Meagher D, ÓLaighin G (2008) Direct measurement of human movement by accelerometry. Med Eng Phys 30(10):1364–1386
Guenterberg E, Ostadabbas S, Ghasemzadeh H, Jafari R (2009) An automatic segmentation technique in body sensor networks based on signal energy. In: Proceedings of the Fourth International Conference on Body Area Networks, 21. New York: ACM Press
Guo T, Yan Z, Aberer K (2012) An adaptive approach for online segmentation of multi-dimensional mobile data. In: Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, ACM, pp 7–14
Harvey N, Zhou Z, Keller JM, Rantz M, He Z (2009) Automated Estimation of Elder Activity Levels from Anonymized Video Data. Conf Proc IEEE Eng Med Biol Soc. 2009:7236–9
Hsu YL, Chu CL, Tsai YJ, Wang JS (2015) An inertial pen with dynamic time warping recognizer for handwriting and gesture recognition. Sensors J IEEE 15(1):154–163
Huddle J (1998) Trends in inertial systems technology for high accuracy AUV navigation. In: IEEE Symposium on Autonomous Underwater Vehicle Technology pp. 63–73
Junker H, Amft O, Lukowicz P, Tröster G (2008) Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recogn 41(6):2010–2024
Kahol K, Tripathi P, Panchanathan S, Rikakis T (2003) Gesture segmentation in complex motion sequences. In: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), vol. 3, pp II-105-II-108
Kim S, Park G, Yim S, Choi S, Choi S (2009) Gesture-recognizing hand-held interface with vibrotactile feedback for 3D interaction. IEEE Trans Consum Electron 55(3):1169–1177
King A (1998) Inertial navigation-forty years of evolution. GEC Rev 13(3):140–149
Li Q, Zheng J, Tsai A, Zhou Q (2002) Robust endpoint detection and energy normalization for real-time speech and speaker recognition. IEEE Trans Speech Audio Process 10(3):12
Liang RH, Ouhyoung M (1998) A real-time continuous gesture recognition system for sign language. In: IEEE Int Conf Automatic Face and Gesture Recognition, pp. 558–563
Lim JG, Sohn YI, Kwon DS (2007) Real-time Accelerometer Signal Processing of End Point Detection and feature Extraction for Motion Detection. In: Proceedings of the International Federation of Automatic Control-Human Machine System, pp. 4–6
Lim JG, Kim SY, Kwon DS (2009) Pattern recognition-based real-time end point detection specialized for accelerometer signal. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Signapore, 14–17 July 2009
Lim JG, Sharifi F, Kwon DS (2008) Fast and reliable camera-tracked laser pointer system designed for audience. In: 5th Int Conf on Ubiquitous Robots and Ambient Intelligence, pp 529–534
Liu J, Wang Z, Zhong L, Wickramasuriya J, Vasudevan V (2008) uWave: Accelerometer-based personalized gesture recognition. Rice University and Motorola Labs, Houston
Liu J, Zhong L, Wickramasuriya J, Vasudevan V (2009) uWave: Accelerometer-based personalized gesture recognition and its applications. Pervasive Mob Comput 5(6):657–675
Milner B (1999) Handwriting recognition using acceleration-based motion detection. IEE Colloquium on Document Processing and Multimedia, London
Nittono H (2007) Event-related brain potentials corroborate subjectively optimal delay in computer response to a user’s action. In: Harris D (ed) Engineering Psychology and Cognitive Ergonomics. 7th International Conference, EPCE 2007, Held as Part of HCI International 2007, Beijing, China, July 22–27, 2007. Proceedings. Lecture Notes in Computer Science, vol. 4562, Springer Berlin Heidelberg, pp. 575–581
Ojeda L, Borenstein J (2007) Non-GPS navigation for security personnel and first responders. J Navig 60(03):391–407
Oppenheim AV, Schafer RW, Buck JR (1989) Discrete-time signal processing. Prentice-hall, Englewood Cliffs
Pang G, Liu H (2001) Evaluation of a low-cost MEMS accelerometer for distance measurement. J Intell Robot Syst 30(3):249–265
Smith SL, Mosier JN (1986) Guidelines for designing user interface software. Mitre Corporation, Bedford
Starner T, Makhoul J, Schwartz R, Chou G (1994) On-line cursive handwriting recognition using speech recognition methods. Proceedings of IEEE ICASSP, Adelaide, pp 125–128
Suh YS (2006) Attitude estimation by multiple-mode Kalman filters. IEEE Trans Ind Electron 53(4):1386–1389
Tan C-W, Park S (2005) Design of accelerometer-based inertial navigation systems. Instrum Meas IEEE Trans 54(6):2520–2530
Thong Y, Woolfson M, Crowe J, Hayes-Gill B, Jones D (2004) Numerical double integration of acceleration measurements in noise. Measurement 36(1):73–92
Wang J-S, Chuang F-C (2012) An accelerometer-based digital Pen with a trajectory recognition algorithm for handwritten digit and gesture recognition. Ind Electron IEEE Trans 59(7):2998–3007
Woodman OJ (2007) An introduction to inertial navigation. Univ Cambridge Comp Lab Tech Rep UCAMCL-TR-696 14:15
Wu J, Pan G, Zhang D, Qi G, Li S (2009) Gesture recognition with a 3-d accelerometer. In: Zhang D, Portmann M, Tan A-H, Indulska J (eds) Ubiquitous Intelligence and Computing. 6th International Conference, UIC 2009, Brisbane, Australia, July 7–9, 2009. Proceedings. Lecture Notes in Computer Science, vol. 5585, Springer Berlin Heidelberg, pp 25–38
Yin L, Dong M, Duan Y, Deng W, Zhao K, Guo J (2013) A high-performance training-free approach for hand gesture recognition with accelerometer. Multimedia Tools and Applications, pp. 1–22
Zhang G, Shi G, Luo Y, Wong MY, Li WJ, Leong PH, Wong M (2005) Towards an ubiquitous wireless digital writing instrument using MEMS motion sensing technology. IEEE/ASME Proc. Advanced Intelligent Mechatronics, Monterey, pp 24–28
Zhou Y, Jing L, Wang J, Cheng Z (2012) Separator Design of Gesture Signals Based on Adaptive Threshold Using Wearable Sensors. In: Advanced Information Networking and Applications Workshops (WAINA), Fukuoka, 26–29 March 2012, pp. 25–38: Springer. In Ubiquitous intelligence and computing : 25–38. Springer Berlin Heidelberg
Zhou Y, Cheng Z, Jing L (2014) Threshold selection and adjustment for online segmentation of one-stroke finger gestures using single tri-axial accelerometer. Multimedia Tools and Applications, 1–20
Žumer J, Reynaerts D, Boltežar M (2012) An advanced nonlinear model of a low-g MEMS accelerometer for a computer pen. Measurement 45(3):459–468
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2010–0028680). We are grateful to Lee, Kyoung-koo and No, Seung-dae for graphic illustration, and Elmira Yadollahi for proof-reading.
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Lim, J.G., Kim, J. & Kwon, DS. Multidimensional evaluation and analysis of motion segmentation for inertial measurement unit applications. Multimed Tools Appl 75, 10907–10934 (2016). https://doi.org/10.1007/s11042-015-2812-1
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DOI: https://doi.org/10.1007/s11042-015-2812-1