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Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv (CSUR) 43(3):16 Aggarwal JK, Xia L (2014) Human activity recognition from 3d data: a review. Pattern Recogn Lett 48:70–80 Altun K, Barshan B (2010) Human activity recognition using inertial/magnetic sensor units. In: Human behavior understanding, pp 38–51 Argyriou V, Petrou M, Barsky S (2010) Photometric stereo with an arbitrary number of illuminants. Comput Vis Image Underst 114(8):887–900 Avci A, Bosch S, Marin-Perianu M, Marin-Perianu R, Havinga P (2010) Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: Architecture of Computing Systems (ARCS), 2010 23rd International Conference on, pp 1–10 Bidmeshki MM, Jafari R (2013) Low power programmable architecture for periodic activity monitoring. In: Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems, pp 81–88 Bobick AF, Davis JW (2001) The recognition of human movement using temporal templates. IEEE Trans Pattern Anal Mach Intell 23(3):257–267 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 Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167 Cao C, Zhang Y, Lu H (2015) Multi-modal learning for gesture recognition. In: Multimedia and Expo (ICME), 2015 I.E. International Conference on, pp 1–6 Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):790–808 Chen C, Jafari R, Kehtarnavaz N (2015) UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: Proceedings of the IEEE International Conference on Image Processing. Canada Chen C, Jafari R, Kehtarnavaz N (2015) Improving human action recognition using fusion of depth camera and inertial sensors. IEEE Trans Human-Machine Syst 45(1):51–61 Chen C, Jafari R, Kehtarnavaz N (2015) A real-time human action recognition system using depth and inertial sensor fusion. IEEE Sensors J 2015 Chen C, Jafari R, Kehtarnavaz N (2015) Action recognition from depth sequences using depth motion maps-based local binary patterns. In: Applications of Computer Vision (WACV), 2015 I.E. Winter Conference on, pp 1092–1099 Chen C, Kehtarnavaz N, Jafari R (2014) 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, pp 4983–4986 Chen C, Liu K, Jafari R, Kehtarnavaz N (2014) Home-based senior fitness test measurement system using collaborative inertial and depth sensors. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp 4135–4138 Chen C, Liu K, Kehtarnavaz N (2013) Real-time human action recognition based on depth motion maps. J Real-Time Image Proc 1–9 Chen L, Wei H, Ferryman J (2013) A survey of human motion analysis using depth imagery. Pattern Recogn Lett 34(15):1995–2006 Cippitelli E, Gasparrini S, Gambi E, Spinsante S, Wahsleny J, Orhany I, Lindhy T (2015) Time synchronization and data fusion for RGB-depth cameras and inertial sensors in AAL applications. In: Communication Workshop (ICCW), 2015 I.E. International Conference on, pp 265–270 Delachaux B, Rebetez J, Perez-Uribe A, Mejia HFS (2013) Indoor activity recognition by combining one-vs.-all neural network classifiers exploiting wearable and depth sensors. In: Advances in Computational Intelligence, pp 216–223 Destelle F, Ahmadi A, O’Connor NE, Moran K, Chatzitofis A, Zarpalas D, Daras P (2014) Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors. In: Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European, pp 371–375 Eddy SR (2004) What is a hidden Markov model? Nat Biotechnol 22(10):1315–1316 Ermes M, Parkka J, Mantyjarvi J, Korhonen I (2008) Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans Inf Technol Biomed 12(1):20–26 Evangelidis G, Singh G, Horaud R (2014) Skeletal quads: human action recognition using joint quadruples. In: Pattern Recognition (ICPR), 2014 22nd International Conference on, pp 4513–4518 Gasparrini S, Cippitelli E, Gambi E, Spinsante S, Wåhslén J, Orhan I, Lindh T (2016) Proposal and experimental evaluation of fall detection solution based on wearable and depth data fusion. In: ICT Innovations 2015, pp 99–108 Gasparrini S, Cippitelli E, Spinsante S, Gambi E (2014) A depth-based fall detection system using a Kinect® sensor. Sensors 14(2):2756–2775 Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: Computer Vision, 2009 I.E. 12th International Conference on, pp 221–228 Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416 Guan D, Ma T, Yuan W, Lee YK, Jehad Sarkar AM (2011) Review of sensor-based activity recognition systems. IETE Tech Rev 28(5):418–433 Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans Cybernet 43(5):1318–1334 Helten T, Muller M, Seidel HP, Theobalt C (2013) Real-time body tracking with one depth camera and inertial sensors. In: Computer Vision (ICCV), 2013 I.E. International Conference on, pp 1105–1112 http://www.microsoft.com/en-us/kinectforwindows/ Jovanov E, Milenkovic A, Otto C, De Groen PC (2005) A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. J NeuroEng Rehabil 2(1):6 Klaser A, Marszałek M, Schmid C (2008) A spatio-temporal descriptor based on 3d-gradients. In: BMVC 2008-19th British Machine Vision Conference, pp 275–1. British Machine Vision Association Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput Methods Prog Biomed 117(3):489–501 Kwolek B, Kepski M (2015) Improving fall detection by the use of depth sensor and accelerometer. Neurocomputing 168:637–645 Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2–3):107–123 Laptev I, Marszałek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: Computer Vision and Pattern Recognition, 2008. IEEE Conference on, pp 1–8 Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutorials 15(3):1192–1209 Li Q, Stankovic J, Hanson M, Barth AT, Lach J, Zhou G (2009) Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International Workshop on, pp 138–143 Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3d points. In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 I.E. Computer Society Conference on, pp 9–14 Liu K, Chen C, Jafari R, Kehtarnavaz N (2014) Fusion of inertial and depth sensor data for robust hand gesture recognition. IEEE Sensors J 14(6):1898–1903 Liu K, Chen C, Jafari R, Kehtarnavaz N (2014) Multi-HMM classification for hand gesture recognition using two differing modality sensors. In: Circuits and Systems Conference (DCAS), 2014 I.E. Dallas, pp 1–4 Mukherjee S, Biswas SK, Mukherjee DP (2011) Recognizing human action at a distance in video by key poses. IEEE Trans Circuits Syst Video Technol 21(9):1228–1241 Ni B, Wang G, Moulin P (2013) Rgbd-hudaact: a color-depth video database for human daily activity recognition. In: Consumer Depth Cameras for Computer Vision, pp 193–208 Ofli F, Chaudhry R, Kurillo G, Vidal R, Bajcsy R (2013) Berkeley mhad: a comprehensive multimodal human action database. In: Applications of Computer Vision (WACV), 2013 I.E. Workshop on, pp 53–60 Oreifej O, Liu Z (2013) Hon4d: histogram of oriented 4d normals for activity recognition from depth sequences. In: Computer Vision and Pattern Recognition (CVPR), 2013 I.E. Conference on, pp 716–723 Pavlovic V, Sharma R, Huang TS (1997) Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Trans Pattern Anal Mach Intell 19(7):677–695 Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990 Ramanathan M, Yau WY, Teoh EK (2014) Human action recognition with video data: research and evaluation challenges. IEEE Trans Human-Machine Syst 44(5):650–663 Ruffieux S, Lalanne D, Mugellini E (2013) ChAirGest: a challenge for multimodal mid-air gesture recognition for close HCI. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp 483–488 Schüldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 3, pp 32–36 Shafer G (1976) A mathematical theory of evidence, vol 1. Princeton University Press, Princeton Shan J, Akella S (2014) 3D human action segmentation and recognition using pose kinetic energy. In: Advanced Robotics and its Social Impacts (ARSO), 2014 I.E. Workshop on, pp 69–75 Shotton J, Sharp T, Kipman A, Fitzgibbon A, Finocchio M, Blake A, Moore R (2013) Real-time human pose recognition in parts from single depth images. Commun ACM 56(1):116–124 Spriggs EH, De La Torre F, Hebert M (2009) Temporal segmentation and activity classification from first-person sensing. In: Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on, pp 17–24 Stein S, McKenna SJ (2013) Combining embedded accelerometers with computer vision for recognizing food preparation activities. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp 729–738 Sun L, Aizawa K (2013) Action recognition using invariant features under unexampled viewing conditions. In: Proceedings of the 21st ACM International Conference on Multimedia, pp 389–392 Theodoridis T, Agapitos A, Hu H, Lucas SM (2008) Ubiquitous robotics in physical human action recognition: a comparison between dynamic anns and gp. In: Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pp 3064–3069 Tian Y, Meng X, Tao D, Liu D, Feng C (2015) Upper limb motion tracking with the integration of IMU and Kinect. Neurocomputing 159:207–218 Vemulapalli R, Arrate F, Chellappa R (2014) Human action recognition by representing 3d skeletons as points in a lie group. In: Computer Vision and Pattern Recognition (CVPR), 2014 I.E. Conference on, pp 588–595 Vieira AW, Nascimento ER, Oliveira GL, Liu Z, Campos MF (2012) Stop: space-time occupancy patterns for 3d action recognition from depth map sequences. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp 252–259 Wang J, Liu Z, Chorowski J, Chen Z, Wu Y (2012) Robust 3d Action Recognition with Random Occupancy Patterns. In: Computer Vision–ECCV 2012, pp 872–885 Wang J, Liu Z, Wu Y, Yuan J (2012) Mining actionlet ensemble for action recognition with depth cameras. In: Computer Vision and Pattern Recognition (CVPR), 2012 I.E. Conference on, pp 1290–1297 Weinland D, Ronfard R, Boyer E (2011) A survey of vision-based methods for action representation, segmentation and recognition. Comput Vis Image Underst 115(2):224–241 Wong C, McKeague S, Correa J, Liu J, Yang G Z (2012) Enhanced classification of abnormal gait using BSN and depth. In: Wearable and Implantable Body Sensor Networks (BSN), 2012 Ninth International Conference on, pp 166–171 Wu J, Cheng J (2014) Bayesian co-boosting for multi-modal gesture recognition. J Mach Learn Res 15(1):3013–3036 Xia L, Aggarwal JK (2013) Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: Computer Vision and Pattern Recognition (CVPR), 2013 I.E. Conference on, pp 2834–2841 Xie S, Wang Y (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel Pers Commun 78(1):231–246 Yang AY, Iyengar S, Sastry S, Bajcsy R, Kuryloski P, Jafari R (2008) Distributed segmentation and classification of human actions using a wearable motion sensor network. In: Computer Vision and Pattern Recognition Workshops, 2008. CVPRW’08. IEEE Computer Society Conference on, pp 1–8 Yang AY, Jafari R, Sastry SS, Bajcsy R (2009) Distributed recognition of human actions using wearable motion sensor networks. J Ambient Intell Smart Environ 1(2):103–115 Yang X, Tian Y (2012) Eigenjoints-based action recognition using naive-bayes-nearest-neighbor. In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 I.E. Computer Society Conference on, pp 14–19 Yang X, Tian Y (2014) Super normal vector for activity recognition using depth sequences. In: Computer Vision and Pattern Recognition (CVPR), 2014 I.E. Conference on, pp 804–811 Yang X, Zhang C, Tian Y (2012) Recognizing actions using depth motion maps-based histograms of oriented gradients. In: Proceedings of the 20th ACM International Conference on Multimedia, pp 1057–1060 Ye M, Zhang Q, Wang L, Zhu J, Yang R, Gall J (2013) A survey on human motion analysis from depth data. In: Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Springer Berlin Heidelberg, pp 149–187 Yin Y, Davis R (2013) Gesture spotting and recognition using salience detection and concatenated hidden markov models. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp 489–494 Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition?. In: Computer Vision (ICCV), 2011 I.E. International Conference on, pp 471–478