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研究生:莊芳甄
研究生(外文):Fang-ChenChuang
論文名稱:以慣性元件為基礎之手部動作辨識及身體活動熱量消耗估測技術開發
論文名稱(外文):Development of Inertial-Sensor-Based Hand Gesture Recognition and Physical Activity Energy Expenditure Estimation Technologies
指導教授:王振興王振興引用關係
指導教授(外文):Jeen-Shing Wang
學位類別:博士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:105
中文關鍵詞:加速度計動作辨識手勢辨識熱量消耗估測
外文關鍵詞:AccelerometerActivity recognitionHand gesture recognitionEnergy expenditure estimation
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本論文提出以慣性元件為基礎發展手部動作辨識及身體活動熱量消耗估測技術。首先,本論文開發一基於加速度計之數位筆及其軌跡辨識演算法應用於手寫數字與手勢辨識。軌跡辨識演算法先從加速度訊號擷取時域與頻域之特徵值,之後結合kernel-based class separability方法與線性識別分析法來選取最具意義之特徵值,並利用機率類神經網路成功地辨識平面書寫的數字與三維空間的手勢。此外,本論文開發第一代可穿戴式活動感測系統及其活動分類演算法,用以調查感測器配戴個數於活動辨識率之影響:當感測器擺放於身體不同位置時,找出一組最佳組合可使配戴個數較少且活動辨識率為最佳。再者,基於第一代感測系統之開發經驗,本論文改良與發展第二代可穿戴式活動感測系統及其日常活動分類技術。此日常活動分類技術之優點在於利用各個擊破(divide-and-conquer)的概念,進一步提升不同活動強度(靜止至劇烈)之活動類別的辨識率。最後,本論文開發三種代謝當量估測演算法(代謝當量迴歸模型、指數代謝當量估測方程式與遞迴式類神經網路之代謝當量估測法)來估算不同活動強度之熱量消耗。其中,以遞迴式類神經網路所發展之估測演算法估測效果最佳,不僅大幅改善於非穩態之估測誤差,而且於穩態及非穩態皆能得到令人滿意的精準度。
This dissertation presents the development of inertial-sensor-based hand gesture recognition and physical activity energy expenditure estimation technologies. First, an accelerometer-based digital pen with a trajectory recognition algorithm for handwritten digit and gesture recognition is proposed. The recognition algorithm first extracts the time- and frequency-domain features from the acceleration signals, and then further identifies the most important features by a hybrid method: kernel-based class separability (KBCS) for selecting significant features, and linear discriminant analysis (LDA) for reducing the dimension of features. The reduced features are sent to a trained probabilistic neural network (PNN) for recognition. In addition, a wearable activity sensor system and its activity classification algorithm are proposed to investigate possible combinations of different sensor placements, and identify an optimal combination to achieve satisfactory classification performance. Subsequently, a wearable activity sensor system and its physical activity classification scheme are proposed to improve the classification accuracy. This classification scheme applies a divide-and-conquer strategy for classifying activities of intensities ranging from sedentary to vigorous with high accuracy. Finally, different metabolic equivalent of task (MET) estimation methods, 1) MET regression models, 2) a mono-exponential MET estimation equation, and 3) MET estimation using a recurrent neural network (RNN), are developed to predict MET for a wide range of daily physical activities by the sensor system. The results show that the performance of MET estimation using the RNN outperforms other methods. The proposed method successfully reduces estimation errors in non-steady and steady states
中 文 摘 要 I
ABSTRACT II
致謝 III
ACKNOWLEDGMENT IV
CONTENTS V
LIST OF TABLES VIII
LIST OF FIGURES X
LIST OF ABBREVIATIONS XII
Chapter 1 Introduction 1
1.1 Motivation and Literature Survey 1
1.2 Contributions of the Dissertation 7
1.3 Organization of the Dissertation 10
Chapter 2 An Accelerometer-Based Digital Pen with a Trajectory
Recognition Algorithm for Handwritten Digit and
Gesture Recognition ……………………………………………...11
2.1 Introduction 11
2.2 Hardware Design of Digital Pen 13
2.3 Trajectory Recognition Algorithm 14
2.3.1 Signal Pre-processing 15
2.3.2 Feature Generation 16
2.3.3 Feature Selection 17
2.3.4 Feature Extraction 18
2.3.5 Classifier Construction 20
2.3.6 Summary of the Trajectory Recognition Algorithm 21
2.4 Experimental Results 22
2.4.1 Handwritten Digit Recognition 22
2.4.2 Gesture Recognition 28
2.5 Summary 30
Chapter 3 Physical Activity Classification Algorithm and
Performance Comparison of Different Sensor
Placement by a Wearable Activity Sensor System …………...31
3.1 Introduction 31
3.2 Wearable Activity Sensor System (CILS ver. 01) 33
3.3 Physical Activity Classification Algorithm 34
3.3.1 Signal Pre-processing 34
3.3.2 Feature Generation 36
3.3.3 K-Nearest Neighbor (K-NN) Classifier 38
3.3.4 Summary of the Physical Activity Classification Algorithm 39
3.4 Experimental Results 39
3.4.1 Participants 39
3.4.2 Experimental Procedures 40
3.4.3 Experimental Results 40
3.5 Summary 44
Chapter 4 Activity Classification Scheme for Daily Physical Activity
Analysis by a Wearable Activity Sensor System …………...45
4.1 Introduction 45
4.2 Wearable Activity Sensor System (CILS ver. 02) 47
4.3 Physical Activity Classification Scheme 49
4.3.1 Four Activity Classification Methods 50
4.3.2 Physical Activity Classification Scheme 56
4.4 Experimental Results 60
4.4.1 Participants 60
4.4.2 Experimental Procedures 60
4.4.3 Experimental Results 61
4.5 Summary 66
Chapter 5 Development of MET Estimation Methods for Different
Intensity of Daily Physical Activity by a Wearable
Activity Sensor System ………………………………………...67
5.1 Introduction 67
5.2 Experimental Protocol 70
5.2.1 Hardware Devices 71
5.2.2 Participants 72
5.2.3 Experimental Procedures 72
5.3 MET Estimation Methods 73
5.3.1 MET Regression Models Estimation 74
5.3.2 A Mono-exponential MET Estimation Equation 75
5.3.3 MET Estimation Using a Recurrent Neural Network 77
5.4 Experimental Results 78
5.4.1 Results of MET Regression Models Estimation 79
5.4.2 Results of the Mono-exponential MET Estimation Equation 81
5.4.3 Results of MET Estimation Using the RNN 85
5.5 Summary 88
Chapter 6 Conclusions and Future Work 90
6.1 Conclusions 90
6.2 Recommendations for Future Work 92
6.2.1 Hand Motion Trajectories Recognition 93
6.2.2 Daily Physical Activity Classification 93
6.2.3 Energy Expenditure (EE) Estimation 94
References …96


[1]B. H. Ahn, M. B. Trageser, E. H. Metzger, W. L. Pondrom, M. J. Hadfield, Raymond Carroll, J. D. Coccoli, J. Feldman, S. Helfant, etc., “Inertial technology for the future, IEEE Transaction Aerospace and Electronic Systems, vol. AES-20, no. 4, pp. 414-444, 1984.
[2]F. R. Allen, E. Ambikairajah, N. H. Lovell, and B. G. Celler, “Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models, Physiological Measurement, vol. 27, no. 10, pp. 935-951, 2006.
[3]W. T. Ang, P. K. Khosla, and C. N. Riviere, “Nonlinear regression model of a low-g MEMS accelerometer, IEEE Sensors Journal, vol. 7, no. 1, pp. 81-88, 2007.
[4]P. Asare, “A sign of the times: A composite input device for human-computer interactions, IEEE Potentials, vol. 29, no. 2, pp. 9-14, 2010.
[5]L. Atallah, B. Lo, R. King, and G. Z. Yang, “Sensor positioning for activity recognition using wearable accelerometers, IEEE Transaction Biomedical Circuits and Systems, vol. 5, no. 4, pp. 320-329, 2011.
[6]J. Baek and B. J. Yun, “A sequence-action recognition applying state machine for user interface, IEEE Transaction Consumer Electronics, vol. 54, no. 2, pp. 719-726, 2008.
[7]W. C. Bang, W. Chang, K. H. Kang, E. S. Choi, A. Potanin, and D. Y. Kim, “Self-contained spatial input device for wearable computers, in Proc. of 7th IEEE Int’l Symposium on Wearable Computers, pp. 26-34, 2003.
[8]L. Bao and S. S. Intille, “Activity recognition from user-annotated acceleration data, Lecture Notes in Computer Science, pp. 1-17, 2004.
[9]R. Baron and R. Plamondon, “Acceleration measurement with an instrumented pen for signature verification and handwriting analysis, IEEE Transaction Instrumentation and Measurement, vol. 38, no. 6, pp. 1132-1138, 1989.
[10]D. R. Jr Bassett, B. E. Ainsworth, A. M. Swartz, S. J. Strath, W. L. O’Brien, and G. A. King, “Validity of four motion sensors in measuring moderate intensity physical activity, Medicine & Science in Sports & Exercise, vol. 32, no. 9, pp. S471-S480, 2000.
[11]D. R. Bouchard and F. Trudeau, “Estimation of energy expenditure in a work environment: Comparison of accelerometry and oxygen consumption/heart rate regression, Ergonomics, vol. 51, no. 5, pp. 663-670, 2008..
[12]S. Brage, N. Wedderkopp, P. W. Franks, L. B. Andersen, and K. Froberg, “Reexamination of validity and reliability of the CSA monitor in walking and running, Medicine & Science in Sports & Exercise, vol. 35, no. 8, pp. 1447-1454, 2003.
[13]L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth, Mar. 1984.
[14]A. G. Brooks, S. M. Gunn, R. T. Withers, C. J. Gore, and J. L. Plummer, “Predicting walking METs and energy expenditure from speed or accelerometry, Medicine & Science in Sports & Exercise, vol. 37, no. 7, pp. 1216-1223, 2005.
[15]K. L. Campbell, P. R. E. Crocker, and D. C. Mckenzie, “Field evaluation of energy expenditure in women using Tritrac accelerometers, Medicine & Science in Sports & Exercise, vol. 34, no. 10, pp. 1667-1674, 2002.
[16]K. Y. Chen and M. Sun, “Improving energy expenditure estimation by using a triaxial accelerometer, Journal of Applied Physiology, vol. 83, pp. 2112-2122, 1997.
[17]K. Y. Chen and D. R. Bassett, “The technology of accelerometry-based activity monitors: Current and future, Medicine & Science in Sports & Exercise, vol. 37, no. 11, pp. S490-S500, 2005.
[18]Y. P. Chen, J. Y. Yang, S. N. Liou, G. Y. Lee, and J. S. Wang, “Online classifier construction algorithm for human activity detection using a tri-axial accelerometer, Applied Mathematics and Computation, vol. 205, no. 2, pp. 849-860, 2008.
[19]S. J. Cho, J. K. Oh, W. C. Bang, W. Chang, E. Choi, Y. Jing, J. Cho, and D. Y. Kim, “Magic wand: A hand-drawn gesture input device in 3-D space with inertial sensors, in Proc. of IEEE 9th Int’l Workshop on Frontiers in Handwriting Recognition, pp. 106-111, 2004.
[20]S. D. Choi, A. S. Lee, and S. Y. Lee, “On-line handwritten character recognition with 3D accelerometer, in Proc. of IEEE Int’l Conf. Information Acquisition, pp. 845-850, 2006.
[21]T. W. Christensen, “Autonomous upgrading of inertial navigation system, IEEE Transaction Aerospace and Electronic Systems, vol. AES-3, no. 4, pp. 623-636, 1967.
[22]T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification, IEEE Transaction Information Theory, vol. IT-13, pp. 21-27, 1967.
[23]S. E. Crouter, K. G. Clowers, and D. R. Jr. Bassett, “A novel method for using accelerometer data to predict energy expenditure, Journal of Applied Physiology, vol. 100, no. 4, pp. 1324-1331, 2006.
[24]S. E. Crouter, J. R. Churilla, and D. R. Jr. Bassett, “Estimating energy expenditure using accelerometers, Journal of Applied Physiology, vol. 98, no. 6, pp. 601-612, 2006.
[25]S. E. Crouter and D. R. Bassett Jr, “A new 2-regression model for the Actical accelerometer, British Journal of Sports Medicine, vol. 42, no. 3, pp. 217-224, 2008.
[26]D. Curone, G. M. Bertolotti, A. Cristiani, E. L. Secco, and G. Magenes, “A real-time and self-calibrating algorithm based on triaxial accelerometer signals for the detection of human posture and activity, IEEE Transaction Information Technology in Biomedicine, vol. 14, no. 4, pp. 1098-1105, 2010.
[27]A. Czabke, S. Marsch, and T. C. Lueth, “Accelerometer based real-time activity analysis on a microcontroller, in Proc. of 2011 5th Int’l Conf. on Pervasive Computing Technologies for Healthcare, pp. 40-46, 2011.
[28]Z. Dong, G. Zhang, C. C. Tsang, G. Shi, W. J. Li, P. H. W. Leong, and M. Y. Wong, “μIMU-based handwriting recognition calibration by optical-tracking, in Proc. of 2007 IEEE Int’l Conf. on Robotics and Biomimetics, pp. 382-387, 2007.
[29]L. Dong, J. Wu, and X. Chen, “Real-time physical activity monitoring by data fusion in body sensor networks, in Proc. of 10th IEEE Int’l Conf. on Information Fusion, pp. 1-7, 2007.
[30]Z. Dong, U. C. Wejinya, and W. J. Li, “An optical-tracking calibration method for MEMS-based digital writing instrument, IEEE Sensors Journal, vol. 10, no. 10, pp. 1543-1551, 2010.
[31]C. A. Dorminy, L. Choi, S. A. Akohoue, K. Y. Chen, and M. S. Buchowski, “Validity of a multisensor armband in estimating 24h energy expenditure in children, Medicine & Science in Sports & Exercise, vol. 40, no. 4, pp. 699-706, 2008.
[32]M. Ermes, J. Parkka, J. Mantyjarvi, and I. Korhonen, “Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions, IEEE Transaction Information Technology in Biomedicine, vol. 12, no. 1, pp. 20-26, 2008.
[33]T. Frantti and S. Kallio, “Expert system for gesture recognition in terminal’s user interface, Expert Systems with Applications, vol. 26, no. 2, pp. 189-202, 2004.
[34]P. S. Freedson, E. Melanson, and J. Sirard, “Calibration of the Computer Science and Applications, Inc. accelerometer, Medicine & Science in Sports & Exercise, vol. 30, no. 5, pp. 777-781, 1998.
[35]R. A. Groeneveld and G. Meeden, “Measuring skewness and kurtosis, The Statistician, vol. 33, pp. 391-399, 1984.
[36]I. C. Gyllensten and A. G. Bonomi, “Identifying types of physical activity with a single accelerometer: Evaluating laboratory-trained algorithms in daily life, IEEE Transaction Biomedical Engineering, vol. 58, no. 9, pp. 2656-2663, 2011.
[37]M. J. Hadfield and K. E. Leiser, “Application, integration and operational aspects of an inertial navigation/survey/pointing system, in Proc. of 1989 IEEE Conf. Record on Vehicle Navigation and Information Systems Conf., pp. 225-233, 1989.
[38]D. P. Heil, B. K. Higginson, C. P. Keller, and C. A. Juergens, “Equipment testing and validation body size as a determinant of activity monitor output during overground walking, Journal of Exercise Physiology Online, vol. 6, pp. 1-11, 2003.
[39]D. P. Heil and N. J. Klippel, “Validation of energy expenditure prediction algorithms in adolescents and teens using the Actical activity monitor, Medicine & Science in Sports & Exercise, vol. 35, pp. S285, 2003.
[40]D. P. Heil, “Predicting activity energy expenditure using the Actical activity monitor, Research Quarterly for Exercise and Sport, vol. 77, no. 1, pp 64-80, 2006.
[41]D. Hendelman, K. Miller, C. Baggett, E. Debold, and P. Freedson, “Validity of accelerometry for the assessment of moderate intensity physical activity in the Weld, Medicine & Science in Sports & Exercise, vol. 32, no. 9, pp. S442-S449, 2000.
[42]Y. L. Hsu and J. S. Wang, “A Wiener-type recurrent neural network and its control strategy for nonlinear dynamic applications, Journal of Process Control, vol. 19, no. 6, pp. 942-953, 2009.
[43]E. Jequire and Y. Schutz, “Long-term measurements of energy expenditure in humans using a respiration chamber, The American Journal Clinical Nutrition, vol. 38, pp. 989-998, 1983.
[44]D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovrll, and B. G. Celler, “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring, IEEE Transaction Information Technology in Biomedicine, vol. 10, no. 1, pp. 156-167, 2006.
[45]A. M. Khan, Y. K. Lee, S. Y. Lee, and T. S. Kim, “A triaxial accelerometer-based physical-activity recognition via augmented-signal feature and a hierarchical recognizer, IEEE Transaction Information Technology in Biomedicine, vol. 14, no. 5, pp. 1166-1172, 2010.
[46]S. Kim, G. Park, S. Yim, S. Choi, and S. Choi, “Gesture-recognizing hand-held interface with vibrotactile feedback for 3D interaction, IEEE Transaction Consumer Electronics, vol. 55, no. 3, pp. 1169-1177, 2009.
[47]N. C. Krishnan, C. Juillard, D. Colbry, and S. Panchanathan, “Recognition of hand movements using wearable accelerometers, Journal of Ambient Intelligence and Smart Environments, vol. 1, no. 2, pp. 143-155, 2009.
[48]H. Kumahara, Y. Schutz, M. Ayabe, M. Yoshioka, Y. Yoshitake, M. Shindo, K. Ishii, and H. Tanaka, “The use of uniaxial accelerometry for the assessment of physical-activity-related energy expenditure: a validation study against whole-body indirect calorimetry, British Journal of Nutrition, vol. 91, no. 2, pp. 235-243, 2004.
[49]S. Koga, T. Shiojiri, Y. Fukuba, Y. Fukuoka, and N. Kondo, “Pulmonary oxygen uptake kinetics in non-steady state, Journal of Physiological Anthropology, vol. 15, no. 1, pp. 1-4, 1996.
[50]S. H. Lee, H. D. Park, S. Y. Hong, K. J. Lee, and Y. H. Kim, “A study on the activity classification using a triaxial accelerometer, in Proc. of IEEE 25th Annual Int’l Conf. on Engineering in Medicine and Biology Society, vol. 3, pp. 2941-2943, 2003.
[51]J. G. Lim, S. Y. Kim, and D. S. Kwon, “Pattern recognition-based real-time end point detection specialized for accelerometer signal, in Proc. of IEEE/ASME Int’l Conf. Advanced Intelligent Mechatronics, pp. 203-208, 2009.
[52]J. Liu, L. Zhong, J. Wickramasuriya, and V. Vasudevan, “uWave: Accelerometer-based personalized gesture recognition and its applications, Pervasive and Mobile Computing, vol. 5, no. 6, pp. 657-675, 2009.
[53]Y. Luo, C. C. Tsang, G. Zhang, Z. Dong, G. Shi, S. Y. Kwok, W. J. Li, P. H. W. Leong, and M. Y. Wong, “An attitude compensation technique for a MEMS motion sensor based digital writing instrument, in Proc. IEEE Int’l Conf. on Nano/Micro Engineered and Molecular Systems, pp. 909-914, 2006.
[54]G. M. Lyons, K. M. Culhane, D. Hilton, P. A. Grace, and D. Lyons, “A description of an accelerometer-based mobility monitoring technique, Medical Engineering & Physics, vol. 27, no. 6, pp. 497-504, 2005.
[55]N. Y. Leenders, T. E. Nelson, and W. M. Sherman, “Ability of different physical activity monitors to detect movement during treadmill walking, International Journal of Sports Medicine, vol. 24, no. 1, pp. 43-50, 2003.
[56]A. M. Martinez and A. C. Kak, “PCA versus LDA, IEEE Transaction Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001.
[57]M. J. Mathie, A. C. F. Coster, N. H. Lovell, and B. G. Celler, “Accelerometry: Providing an integrated, practical method for long-term, ambulatory monitoring of human movement, Physiological Measurement, vol. 25, no. 2, pp. R1-R20, 2004.
[58]M. J. Mathie, B. G. Celler, N. H. Lovell, and A. C. F. Coster, “Classification of basic daily movements using a triaxial accelerometer, Medical and Biological Engineering and Computing, vol. 42, no. 5, pp. 679-687, 2004.
[59]U. Maurer, A. Rowe, A. Smailagic, and D. Siewiorek, “Location and activity recognition using eWatch: a wearable sensor platform, Lecture Notes in Computer Science, vol. 3864 , pp. 86-102, 2006.
[60]U. Maurer, A. Smailagic, D. P. Sieweorek, and M. Deisher, “Activity recognition and monitoring using multiple sensors on different body positions, in Proc. of IEEE Int’l Workshop on Wearable and Implantable Body Sensor Networks, pp. 113-116, 2006.
[61]B. Milner, “Probabilistic neural networks, IEE Colloquium on Document Image Processing and Multimedia, pp. 5/1-5/6, 1999.
[62]S. J. Morris and J. A. Paradiso, “Shoe-integrated sensor system for wireless gait analysis and real-time feedback, in Proc. of IEEE Second Joint EMBS/BMES Conf. on Engineering in Medicine and Biology, pp. 2468-2469, 2002.
[63]B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew, C. J. Bula, and P. Robert, “Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly, IEEE Transaction Biomedical Engineering, vol. 50, no. 6, pp. 711-723, 2003.
[64]J. F. Nichols, C. G. Morgan, L. E. Chabot, J. F. Sallis, and K. J. Calfas, “Assessment of physical activity with the Computer Science and Applications, Inc., accelerometer: laboratory versus Weld validation, Research Quarterly for Exercise and Sport, vol. 71, no. 1, pp. 36-43, 2000.
[65]J. K Oh, S. J. Cho, W. C. Bang, W. Chang, E. Choi, J. Yang, J. Cho, and D. Y. Kim, “Inertial sensor based recognition of 3-D characters gestures with an ensemble of classifiers, in Proc. of IEEE 9th Int’l Workshop on Frontiers in Handwriting Recognition, pp. 112-117, 2004.
[66]N. Oliver and F. Flores-Mangas, “Healthgear: A real-time wearable system for monitoring and analyzing physiological signals, in Int’l Workshop on Wearable and Implantable Body Sensor Networks, pp. 61-64, 2006.
[67]J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, and I. Korhonen, “Activity classification using realistic data from wearable sensors, IEEE Transaction Information Technology in Biomedicine, vol. 10, no. 1, pp. 119-128, 2006.
[68]P. Pirttikangas, K. Fujinami, and T. Nakajima, “Feature selection and activity recognition from wearable sensors, Lecture Notes in Computer Science, vol. 4239, pp. 516-527, 2006.
[69]G. Plasqui, A. M. C. P. Joosen, A. D. Kester, A. H. C. Goris, and K. R. Westerterp, “Measuring free-living energy expenditure and physical activity with triaxial accelerometer, Obesity Research, vol. 13, no. 8, pp. 1363-1369, 2005.
[70]S. J. Preece, J. Y. Goulermas, L. P. J. Kenney, and D. Howard, “A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data, IEEE Transaction Biomedical Engineering, vol. 56, no. 3, pp. 871-879, 2009.
[71]E. Prugovecki, Quantum Mechanics in Hilbert Space. Academic Press, 1981.
[72]M. R. Puyau, A. L. Adolph, F. A. Vohra, I. Zakeri, and N. F. Butte, “Prediction of activity energy expenditure using accelerometers in children, Medicine & Science in Sports & Exercise, vol. 36, no. 9, pp. 1625-1631, 2004.
[73]D. M. Pober, J. Staudenmayer, C. Raphael, and P. S. Freedson, “Development of novel techniques to classify physical activity mode using accelerometers, Medicine & Science in Sports & Exercise, vol. 38, no. 9, pp. 1626-1634, 2006.
[74]N. Ravi, N. Dandekar, P. Mysore, and M. Littman, “Activity recognition from accelerometer data, in Proc. of the 7th Innovative Applications of Artificial Intelligence Conf., pp. 1541-1546, 2005.
[75]R. S. Rawson and T. M. Walsh, “Estimation of resistance exercise energy expenditure using accelerometry, Medicine & Science in Sports & Exercise, vol. 42, no. 3, pp. 622-628, 2010.
[76]H. Rob and F. Yan, “Sample quantiles in statistical packages, American Statistician, vol. 50, no. 4, pp. 361-365, 1996.
[77]G. Rodriguez, L. Michaud, L. A. Moreno, D. Turck, and F. Gottrand, “Comparison of the TriTrac-R3D accelerometer and a self-report activity diary with heart-rate monitoring for the assessment of energy expenditure in children, British Journal of Nutrition, vol. 87, no. 6, pp. 623-631, 2002.
[78]M. P. Rothney, M. Neumann, and A. Béziat, “An artificial neural network model of energy expenditure using nonintegrated acceleration signals, Journal of Applied Physiology, vol. 103, no. 4, pp. 1419-1427, 2007.
[79]M. P. Rothney, E. V. Schaefer, M. M. Neumann, L. Choi, and K. Y. Chen, “Validity of physical activity intensity predictions by Actigraph, Actical, and RT3 accelerometers, Obesity Society, vol. 16, no. 8, pp. 1946-1952, 2008.
[80]J. B. Saunders, V. Inman, and H. D. Eberhart, “The major determinants in normal and pathological gait, The Journal of Bone & Joint Surgery, vol. 35, pp. 543-558, 1953.
[81]S. Schulz, K. R. Westerterp, and K. Brück, “Comparison of energy expenditure by the doubly labeled water technique with energy intake, heart rate, and activity recording in man, American Society for Clinical Nutrition, vol. 49, no. 6, pp. 1146-1154, 1989.
[82]D. F. Specht, “Probabilistic neural networks, Neural Networks, vol. 3, pp. 109-118, 1990.
[83]J. Staudenmayer, D. Pober, S. Crouter, D. Bassett, and P. Freedson, An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer, Journal of Applied Physiology, vol. 107, no. 4, pp. 1300-1307, 2009.
[84]S. J. Strath, D. R. Basseett, A. M. Swartz, and D. L. Thompson, “Simultaneous heart rate-motion sensor technique to estimate energy expenditure, Medicine & Science in Sports & Exercise, vol. 33, no. 12, pp. 2118-2123, 2001.
[85]S. W. Su, L. Wang, B. G. Celler, and A. V. Savkin, “Oxygen uptake estimation in humans during exercise using a Hammerstein model, Annals of Biomedical Engineering, vol. 35, no. 11, pp. 1898-1906, 2007.
[86]S. W. Su, B. G. Celler, A. V. Savkin, H. T. Nguyen, T. M. Cheng, Y. Guo, and L. Wang, “Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling, in Proc. of 30th Annals Int’l Conf. of IEEE Engineering in Medicine Biology Society, pp. 2431-2434, 2008.
[87]Y. S. Suh, “Attitude estimation by multiple-mode Kalman filters, IEEE Transaction Industrial Electronics, vol. 53, no. 4, pp. 1386-1389, 2006.
[88]A. M. Swartz, S. J. Strath, D. R. Jr. Bassett, W. L. O’Brien, G. A. King, and B. E. Ainsworth, “Estimation of energy expenditure using CSA accelerometers at hip and wrist sites, Medicine & Science in Sports & Exercise, vol. 32, no. 9, pp. S450-S456, 2000.
[89]E. M. Tapia, Using machine learning for real-time activity recognition and estimation of energy expenditure, Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts, U. S. A, 2008.
[90]The MathWorks Inc., “Statistics ToolboxTM User's Guide MathWorks Inc. 3 Apple Hill Drive Natick, MA 01760-2098.
[91]J. A. Toschke, R. V. Kries, and E. Rosenfeld, “Reliability of physical activity measures from accelerometry among preschoolers in free-living conditions, Clinical Nutrition, vol. 26, no. 4, pp. 416-420, 2007.
[92]R. Tranfield, “INS/GPS navigation systems for land applications,in Proc, of IEEE Position Location and Navigation Symposium, pp. 391-398, 1996.
[93]H. Vathsangam, A. Emken, E. T. Schroeder, D. Spruijt-Metz, and G. S. Sukhatme, “Determining energy expenditure from treadmill walking using hip-worn inertial sensors: An experimental study, IEEE Transaction Biomedical Engineering, vol. 58, no. 10, pp. 2804-2815, 2011.
[94]C. Verplaetse, “Inertial proprioceptive devices: Self-motion-sensing toys and tools, IBM Systems Journal, vol. 35, no. 3-4, pp. 639-650, 1996.
[95]L. Wang, “Feature selection with kernel class separability, IEEE Transaction Pattern Analysis and Machine Intelligence, vol. 30, no. 9, pp. 1534-1546, 2008.
[96]J. S. Wang, Y. L. Hsu, and J. N. Liu, “An inertial-measurement-unit-based pen with a trajectory reconstruction algorithm and its applications, IEEE Transaction Industrial Electronics, vol. 57, no. 10, pp. 3508-3521, 2010.
[97]B. Weber, I. Hermanns, R. Ellegast, and J. Kleinert, “A person-centered measurement system for quantification of physical activity and energy expenditure at workplaces, Lecture Notes in Computer Science, vol. 5624, pp. 121-130, 2009.
[98]G. J. Welk, S. N. Blair, K. Wood, S. Jones, and R. W. Thompson, “A comparative evaluation of three accelerometry-based physical activity monitors, Medicine & Science in Sports & Exercise, vol. 32, no. 9, pp. S489-S497, 2000.
[99]G. J. Welk, J. A. Schaben, and J. R. Jr Morrow, “Reliability of accelerometry-based activity monitors: a generalizability study, Medicine & Science in Sports & Exercise, vol. 36, no. 9, pp. 1637-1645, 2004.
[100] A. J. Wixted, D. V. Thiel, A. G. Hahn, C. J. Gore, D. B. Pyne, and D. A. James, “Measurement of energy expenditure in Elite Athletes using MEMS-based triaxial accelerometers, IEEE Sensors Journal, vol. 7, no. 4, pp. 481-488, 2007.
[101] S.-H. P. Won, F. Golnaraghi, and W. W. Melek, “A fastening tool tracking system using an IMU and a position sensor with Kalman filters and a fuzzy expert system, IEEE Transaction Industrial Electronics, vol. 56, no. 5, pp. 1782-1792, 2009.
[102] S.-H. P. Won, W. W. Melek, and F. Golnaraghi, “A Kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system, IEEE Transaction Industrial Electronics, vol. 57, no. 5, pp. 1787-1798, 2010.
[103] T. C. Wong, J. G. Webster, H. J. Montoye, and R. Washburn, “Portable accelerometer device for measuring human energy expenditure, IEEE Transaction Biomedical Engineering, vol. BME-28, no. 6, pp. 467-471, 1981.
[104] J. Yang, W. Chang, W. C. Bang, E. S. Choi, K. H. Kang, S. J. Cho, and D. Y. Kim, “Analysis and compensation of errors in the input device based on inertial sensors, in Proc. IEEE Int’l Conf. on Information Technology: Coding and Computing, pp. 790-796, 2004.
[105] A. Yang, S. Iyengar, S. Sastry, R. Bajcsy, P. Kuruloski, and R. Jafari, “Distributed segmentation and classification of human actions using a wearable sensor network, in the CVPR Workshop on Human Communicative Behavior Analysis, pp. 1-17, 2008.
[106] J. Y. Yang, J. S. Wang, and Y. P. Chen, “Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers, Pattern Recognition Letters, vol. 29, no. 16, pp. 2213-2220, 2008.
[107] A. Yngve, A. Nilsson, M. Sjostrom, and U. Ekelund, “Effect of monitor placement and of activity setting on the MTI accelerometer output, Medicine & Science in Sports & Exercise, vol. 35, no. 2, pp. 320-326, 2003.
[108] K. Zhang, P. Werner, M. Sun, F. X. Pi-Sunyer, and C. N. Boozer, “Measurement of human daily physical activity, Obesity Research, vol. 11, no. 1, pp. 33-40, 2003.
[109] S. Zhou, Z. Dong, W. J. Li, and C. P. Kwong, “Hand-written character recognition using MEMS motion sensing technology, in Proc. of IEEE/ASME Int’l Conf. Advanced Intelligent Mechatronics, pp. 1418-1423, 2008.

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