Cite

high-precision-pressure-sensor-height-sensor-module. https://robu.in/product/gy-63ms5611-01ba03-high-precision-pressure-sensor-height-sensor-module/. high-precision-pressure-sensor-height-sensor-module https://robu.in/product/gy-63ms5611-01ba03-high-precision-pressure-sensor-height-sensor-module/. Search in Google Scholar

“Similar gait action recognition using an inertial sensor”, Pattern Recognition, 48(4):1289–1301, 2015. “Similar gait action recognition using an inertial sensor” Pattern Recognition 48 4 1289 1301 2015 10.1016/j.patcog.2014.10.012 Search in Google Scholar

R. Abdel-Salam, R. Mostafa, and M. Hadhood, “Human activity recognition using wearable sensors: Review, challenges, evaluation benchmark”, ArXiv, abs/2101.01665, 2021. Abdel-SalamR. MostafaR. HadhoodM. “Human activity recognition using wearable sensors: Review, challenges, evaluation benchmark” ArXiv abs/2101.01665, 2021 10.1007/978-981-16-0575-8_1 Search in Google Scholar

J. K. Aggarwal and L. Xia, “Human activity recognition from 3d data: A review”, Pattern Recognition Letters, 48 (Celebrating the life and work of Maria Petrou.):70–80, 2014. AggarwalJ. K. XiaL. “Human activity recognition from 3d data: A review” Pattern Recognition Letters 48 (Celebrating the life and work of Maria Petrou.) 70 80 2014 10.1016/j.patrec.2014.04.011 Search in Google Scholar

N. Ahmad, R. Ariffin Bin Raja Ghazilla, N. Mohd Khairi, and V. Kasi, “Reviews on various inertial measurement unit (imu) sensor applications”, SiPS 2013, 2013. AhmadN. Ariffin Bin Raja GhazillaR. Mohd KhairiN. KasiV. “Reviews on various inertial measurement unit (imu) sensor applications” SiPS 2013 2013 10.12720/ijsps.1.2.256-262 Search in Google Scholar

O. C. Ann and L. B. Theng, “Human activity recognition: A review”, 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014), pp. 389–393, 2014. AnnO. C. ThengL. B. “Human activity recognition: A review” 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014) 389 393 2014 10.1109/ICCSCE.2014.7072750 Search in Google Scholar

D. J. Beddiar, B. Nini, M. Sabokrou, and A. Hadid, “Vision-based human activity recognition: A survey”, Multimedia Tools Appl., 79(41–42):30509–30555, Nov. 2020. BeddiarD. J. NiniB. SabokrouM. HadidA. “Vision-based human activity recognition: A survey” Multimedia Tools Appl 79 41–42 30509 30555 Nov. 2020 10.1007/s11042-020-09004-3 Search in Google Scholar

L. C. Benson, C. A. Clermont, S. T. Osis, D. Kobsar, and R. Ferber, “Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods”, Journal of Biomechanics, 71:94–99, 2018. BensonL. C. ClermontC. A. OsisS. T. KobsarD. FerberR. “Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods” Journal of Biomechanics 71 94 99 2018 10.1016/j.jbiomech.2018.01.03429454542 Search in Google Scholar

A. Bux, P. Angelov, and Z. Habib, “Vision based human activity recognition: A review”, in Plamen Angelov, Alexander Gegov, Chrisina Jayne, and Qiang Shen, editors, Advances in Computational Intelligence Systems, pp. 341–371, Cham, 2017. BuxA. AngelovP. HabibZ. “Vision based human activity recognition: A review” in AngelovPlamen GegovAlexander JayneChrisina ShenQiang editors, Advances in Computational Intelligence Systems 341 371 Cham 2017 10.1007/978-3-319-46562-3_23 Search in Google Scholar

J. Camargo, W. Flanagan, N. Csomay-Shanklin, B. Kanwar, and A. Young, “A machine learning strategy for locomotion classification and parameter estimation using fusion of wearable sensors”, IEEE Transactions on Biomedical Engineering, 68(5):1569–1578, 2021. CamargoJ. FlanaganW. Csomay-ShanklinN. KanwarB. YoungA. “A machine learning strategy for locomotion classification and parameter estimation using fusion of wearable sensors” IEEE Transactions on Biomedical Engineering 68 5 1569 1578 2021 10.1109/TBME.2021.306580933710951 Search in Google Scholar

D. Castro, W. Coral, C. Rodriguez, J. Cabra, and J. Colorado, “Wearable-based human activity recognition using an iot approach”, Journal of Sensor and Actuator Networks, 6(4), 2017. CastroD. CoralW. RodriguezC. CabraJ. ColoradoJ. “Wearable-based human activity recognition using an iot approach” Journal of Sensor and Actuator Networks 6 4 2017 10.3390/jsan6040028 Search in Google Scholar

D. Chen, G. Asaeikheybari, H. Chen, W. Xu, and M.-C. Huang, “Ubiquitous fall hazard identification with smart insole”, IEEE journal of biomedical and health informatics, 2020. ChenD. AsaeikheybariG. ChenH. XuW. HuangM.-C. “Ubiquitous fall hazard identification with smart insole” IEEE journal of biomedical and health informatics 2020 10.1109/JBHI.2020.304670133351772 Search in Google Scholar

D. Chen, Y. Cai, J. Cui, J. Chen, H. Jiang, and M.-C. Huang, “Risk factors identification and visualization or work-related musculoskeletal disorders with wearable and connected gait analytics system and Kinect skeleton models”, SmartHealth, 7:60–77, 2018. ChenD. CaiY. CuiJ. ChenJ. JiangH. HuangM.-C. “Risk factors identification and visualization or work-related musculoskeletal disorders with wearable and connected gait analytics system and Kinect skeleton models” SmartHealth 7 60 77 2018 10.1016/j.smhl.2018.05.003 Search in Google Scholar

D. Chen, Y. Cai, X. Qian, R. Ansari, W. Xu, K.-C. Chu, and M.-C. Huang, “Bring gait lab to everyday life: Gait analysis in terms of activities of daily living”, IEEE Internet of Things Journal, 7(2):1298–1312, 2020. ChenD. CaiY. QianX. AnsariR. XuW. ChuK.-C. HuangM.-C. “Bring gait lab to everyday life: Gait analysis in terms of activities of daily living” IEEE Internet of Things Journal 7 2 1298 1312 2020 10.1109/JIOT.2019.2954387 Search in Google Scholar

D. Chen, H. Cao, H. Chen, Z. Zhu, X. Qian, W. Xu, and M.-C. Huang, “Smart insole-based indoor localization system for internet of things applications”, IEEE Internet of Things Journal, 6(4):7253–7265, 2019. ChenD. CaoH. ChenH. ZhuZ. QianX. XuW. HuangM.-C. “Smart insole-based indoor localization system for internet of things applications” IEEE Internet of Things Journal 6 4 7253 7265 2019 10.1109/JIOT.2019.2915791 Search in Google Scholar

D. Chen, J. Chen, H. Jiang, and M.-C. Huang, “Risk factors identification for work-related musculoskeletal disorders with wearable and connected gait analytics system”, in 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 330–339, IEEE, 2017. ChenD. ChenJ. JiangH. HuangM.-C. “Risk factors identification for work-related musculoskeletal disorders with wearable and connected gait analytics system” in 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 330 339 IEEE 2017 10.1109/CHASE.2017.116 Search in Google Scholar

L. Chen and C. D. Nugent, Sensor-Based Activity Recognition Review, Springer International Publishing, Cham, pp. 23–47, 2019. ChenL. NugentC. D. Sensor-Based Activity Recognition Review Springer International Publishing Cham 23 47 2019 10.1007/978-3-030-19408-6_2 Search in Google Scholar

T. Chu, A. Chua, and E. Secco, “A wearable myo gesture armband controlling sphero bb-8 robot”, HighTech and Innovation Journal, 1, 10, 2020. ChuT. ChuaA. SeccoE. “A wearable myo gesture armband controlling sphero bb-8 robot” HighTech and Innovation Journal 1 10 2020 10.28991/HIJ-2020-01-04-05 Search in Google Scholar

S. Eisa and A. Moreira, “A behaviour monitoring system (bms) for ambient assisted living”, Sensors, 17(9), 2017. EisaS. MoreiraA. “A behaviour monitoring system (bms) for ambient assisted living” Sensors 17 9 2017 10.3390/s17091946562073628837105 Search in Google Scholar

G. Ershadi, M. Gwak, A. Aminian, R. Soangra, M. GrantBeuttler, and M. Sarrafzadeh, “Smart insole: Remote gait detection algorithm using pressure sensors for toe walking rehabilitation”, in 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), pp. 332–337, 2021. ErshadiG. GwakM. AminianA. SoangraR. GrantBeuttlerM. SarrafzadehM. “Smart insole: Remote gait detection algorithm using pressure sensors for toe walking rehabilitation” in 2021 IEEE 7th World Forum on Internet of Things (WF-IoT) 332 337 2021 10.1109/WF-IoT51360.2021.9595676 Search in Google Scholar

A. Gupta, K. Gupta, K. Gupta, and K. Gupta, “A survey on human activity recognition and classification”, in 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0915–0919, 2020. GuptaA. GuptaK. GuptaK. GuptaK. “A survey on human activity recognition and classification” in 2020 International Conference on Communication and Signal Processing (ICCSP) 0915 0919 2020 10.1109/ICCSP48568.2020.9182416 Search in Google Scholar

M. M. Hamdi, M. I. Awad, M. M. Abdelhameed, and F. A. Tolbah, “Lower limb gait activity recognition using inertial measurement units for rehabilitation robotics”, in 2015 International Conference on Advanced Robotics (ICAR), pp. 316–322, 2015. HamdiM. M. AwadM. I. AbdelhameedM. M. TolbahF. A. “Lower limb gait activity recognition using inertial measurement units for rehabilitation robotics” in 2015 International Conference on Advanced Robotics (ICAR) 316 322 2015 10.1109/ICAR.2015.7251474 Search in Google Scholar

G.-M. Jeong, P. H. Truong, and S.-I. Choi, “Classification of three types of walking activities regarding stairs using plantar pressure sensors”, IEEE Sensors Journal, 17(9):2638–2639, 2017. JeongG.-M. TruongP. H. ChoiS.-I. “Classification of three types of walking activities regarding stairs using plantar pressure sensors” IEEE Sensors Journal 17 9 2638 2639 2017 10.1109/JSEN.2017.2682322 Search in Google Scholar

E. Kantoch, “Human activity recognition for physical rehabilitation using wearable sensors fusion and artificial neural networks”, in 2017 Computing in Cardiology (CinC), pp. 1–4, 2017. KantochE. “Human activity recognition for physical rehabilitation using wearable sensors fusion and artificial neural networks” in 2017 Computing in Cardiology (CinC) 1 4 2017 10.22489/CinC.2017.296-332 Search in Google Scholar

O. D. Lara and M. A. Labrador, “A survey on human activity recognition using wearable sensors”, IEEE Communications Surveys Tutorials, 15(3):1192–1209, 2013. LaraO. D. LabradorM. A. “A survey on human activity recognition using wearable sensors” IEEE Communications Surveys Tutorials 15 3 1192 1209 2013 10.1109/SURV.2012.110112.00192 Search in Google Scholar

R. Liu, A. A. Ramli, H. Zhang, E. Datta, and X. Liu, “An overview of human activity recognition using wearable sensors: Healthcare and artificial intelligence”, CoRR, abs/2103.15990, 2021. LiuR. RamliA. A. ZhangH. DattaE. LiuX. “An overview of human activity recognition using wearable sensors: Healthcare and artificial intelligence” CoRR abs/2103.15990, 2021 10.1007/978-3-030-96068-1_1 Search in Google Scholar

X. Liu and Q. Wang, “Incrementally classifying different walking activities based on wearable sensors”, in 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 699–704, 2021. LiuX. WangQ. “Incrementally classifying different walking activities based on wearable sensors” in 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) 699 704 2021 10.1109/M2VIP49856.2021.9665024 Search in Google Scholar

Y. Liu, J. Huang, G. Ding, and Z. Yang, “High-performance and wearable strain sensors based on graphene microfluidics and serpentine microchannels for human motion detection”, Microelectronic Engineering, 231:111402, 2020. LiuY. HuangJ. DingG. YangZ. “High-performance and wearable strain sensors based on graphene microfluidics and serpentine microchannels for human motion detection” Microelectronic Engineering 231 111402 2020 10.1016/j.mee.2020.111402 Search in Google Scholar

I. H. López-Nava, M. Garcia-Constantino, and J. Favela, “Recognition of gait activities using acceleration data from a smartphone and a wearable device”, in UCAmI, 2019. López-NavaI. H. Garcia-ConstantinoM. FavelaJ. “Recognition of gait activities using acceleration data from a smartphone and a wearable device” in UCAmI 2019 10.3390/proceedings2019031060 Search in Google Scholar

I. H. López-Nava, A. Muñoz-Meléndez, A. I. Pérez Sanpablo, A. Alessi Montero, I. Quiñones Urióstegui, and L. Núñez Carrera, “Estimation of temporal gait parameters using bayesian models on acceleration signals”, Computer Methods in Biomechanics and Biomedical Engineering, 19(4):396–403, 2016. PMID: 25876180 López-NavaI. H. Muñoz-MeléndezA. Pérez SanpabloA. I. Alessi MonteroA. Quiñones UriósteguiI. Núñez CarreraL. “Estimation of temporal gait parameters using bayesian models on acceleration signals” Computer Methods in Biomechanics and Biomedical Engineering 19 4 396 403 2016 PMID: 25876180 10.1080/10255842.2015.103294525876180 Search in Google Scholar

C. F. Martindale, V. Christlein, P. Klumpp, and B. M. Eskofier, “Wearables-based multi-task gait and activity segmentation using recurrent neural networks”, Neurocomputing, 432:250–261, 2021. MartindaleC. F. ChristleinV. KlumppP. EskofierB. M. “Wearables-based multi-task gait and activity segmentation using recurrent neural networks” Neurocomputing 432 250 261 2021 10.1016/j.neucom.2020.08.079 Search in Google Scholar

U. Martinez-Hernandez and A. A. Dehghani-Sanij, “Adaptive bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors”, Neural Networks, 102:107–119, 2018. Martinez-HernandezU. Dehghani-SanijA. A. “Adaptive bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors” Neural Networks 102 107 119 2018 10.1016/j.neunet.2018.02.01729567532 Search in Google Scholar

U. Martinez-Hernandez, I. Mahmood, and A. A. Dehghani-Sanij, “Simultaneous bayesian recognition of locomotion and gait phases with wearable sensors”, IEEE Sensors Journal, 18(3):1282–1290, 2018. Martinez-HernandezU. MahmoodI. Dehghani-SanijA. A. “Simultaneous bayesian recognition of locomotion and gait phases with wearable sensors” IEEE Sensors Journal 18 3 1282 1290 2018 10.1109/JSEN.2017.2782181 Search in Google Scholar

O. Mazumder, A. S. Kundu, P. K. Lenka, and S. Bhaumik, “Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton”, Gait Posture, 50:53–59, 2016. MazumderO. KunduA. S. LenkaP. K. BhaumikS. “Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton” Gait Posture 50 53 59 2016 10.1016/j.gaitpost.2016.08.01027585182 Search in Google Scholar

G. McCalmont, P. Morrow, H. Zheng, A. Samara, S. Yasaei, H. Wang, and S. McClean, “ezigait: Toward an ai gait analysis and assistant system”, in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2280–2286, 2018. McCalmontG. MorrowP. ZhengH. SamaraA. YasaeiS. WangH. McCleanS. “ezigait: Toward an ai gait analysis and assistant system” in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2280 2286 2018 10.1109/BIBM.2018.8621176 Search in Google Scholar

L. M. Dang, K. Min, H. Wang, Md. JalilPiran, C. Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey”, Pattern Recognition, 108:107561, 2020. DangL. M. MinK. WangH. JalilPiranMd. LeeC. MoonH. “Sensor-based and vision-based human activity recognition: A comprehensive survey” Pattern Recognition 108 107561, 2020 10.1016/j.patcog.2020.107561 Search in Google Scholar

L. M. Dang, K. Min, H. Wang, Md. JalilPiran, C. Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey”, Pattern Recognition, 108:107561, 2020. DangL. M. MinK. WangH. JalilPiranMd. LeeC. MoonH. “Sensor-based and vision-based human activity recognition: A comprehensive survey” Pattern Recognition 108 107561, 2020 10.1016/j.patcog.2020.107561 Search in Google Scholar

C. M. el Achkar, C. Lenoble-Hoskovec, A. Paraschiv-Ionescu, K. Major, C. Büla, and K. Aminian, “Physical behavior in older persons during daily life: Insights from instrumented shoes”, Sensors, 16:1225, August 2016. el AchkarC. M. Lenoble-HoskovecC. Paraschiv-IonescuA. MajorK. BülaC. AminianK. “Physical behavior in older persons during daily life: Insights from instrumented shoes” Sensors 16 1225 August 2016 10.3390/s16081225501739027527172 Search in Google Scholar

C. M. el Achkar, C. Lenoble-Hoskovec, A. Paraschiv-Ionescu, K. Major, C. Büla, and K. Aminian, “Instrumented shoes for activity classification in the elderly”, Gait Posture, 44:12–17, 2016. el AchkarC. M. Lenoble-HoskovecC. Paraschiv-IonescuA. MajorK. BülaC. AminianK. “Instrumented shoes for activity classification in the elderly” Gait Posture 44 12 17 2016 10.1016/j.gaitpost.2015.10.01627004626 Search in Google Scholar

S. C. Mukhopadhyay, “Wearable sensors for human activity monitoring: A review”, IEEE Sensors Journal, 15(3):1321–1330, 2015. MukhopadhyayS. C. “Wearable sensors for human activity monitoring: A review” IEEE Sensors Journal 15 3 1321 1330 2015 10.1109/JSEN.2014.2370945 Search in Google Scholar

S. C. Mukhopadhyay and T. Islam, “Wearable sensors; applications, design and implementation”, 2017. MukhopadhyayS. C. IslamT. “Wearable sensors; applications, design and implementation” 2017 10.1088/978-0-7503-1505-0 Search in Google Scholar

Y. Ng, X. Jiang, Y. Zhang, S. Shin, and R. Ning, “Automated activity recognition with gait positions using machine learning algorithms”, Engineering, Technology Applied Science Research, 9:4554–4560, August 2019. NgY. JiangX. ZhangY. ShinS. NingR. “Automated activity recognition with gait positions using machine learning algorithms” Engineering, Technology Applied Science Research 9 4554 4560 August 2019 10.48084/etasr.2952 Search in Google Scholar

T. F. Novacheck, “The biomechanics of running”, Gait & posture, 7(1):77–95, 1998. NovacheckT. F. “The biomechanics of running” Gait & posture 7 1 77 95 1998 10.1016/S0966-6362(97)00038-610200378 Search in Google Scholar

C. I. Nwakanma, F. B. Islam, M. P. Maharani, J.-M. Lee, and D.-S. Kim, “Detection and classification of human activity for emergency response in smart factory shop floor”, Applied Sciences, 11(8), 2021. NwakanmaC. I. IslamF. B. MaharaniM. P. LeeJ.-M. KimD.-S. “Detection and classification of human activity for emergency response in smart factory shop floor” Applied Sciences 11 8 2021 10.3390/app11083662 Search in Google Scholar

M. N. Orlin and T. G McPoil, “Plantar Pressure Assessment”, Physical Therapy, 80(4):399–409, April 2000. OrlinM. N. McPoilT. G “Plantar Pressure Assessment” Physical Therapy 80 4 399 409 April 2000 10.1093/ptj/80.4.39910758524 Search in Google Scholar

S. Paraschiakos, R. Cachucho, M. Moed, D. van Heemst, S. Mooijaart, E. Slagboom, A. Knobbe, and M. Beekman, “Activity recognition using wearable sensors for tracking the elderly”, User Modeling and User-Adapted Interaction, July 2020. ParaschiakosS. CachuchoR. MoedM. van HeemstD. MooijaartS. SlagboomE. KnobbeA. BeekmanM. “Activity recognition using wearable sensors for tracking the elderly” User Modeling and User-Adapted Interaction July 2020 10.1007/s11257-020-09268-2 Search in Google Scholar

X. Qian, H. Cheng, D. Chen, Q. Liu, H. Chen, H. Jiang, and M.-C. Huang, “The smart insole: A pilot study of fall detection”, in EAI International Conference on Body Area Networks, pp. 37–49, 2019. QianX. ChengH. ChenD. LiuQ. ChenH. JiangH. HuangM.-C. “The smart insole: A pilot study of fall detection” in EAI International Conference on Body Area Networks 37 49 2019 10.1007/978-3-030-34833-5_4 Search in Google Scholar

J. Rafferty, C. D. Nugent, J. Liu, and L. Chen, “From activity recognition to intention recognition for assisted living within smart homes”, IEEE Transactions on Human-Machine Systems, 47(3):368–379, 2017. RaffertyJ. NugentC. D. LiuJ. ChenL. “From activity recognition to intention recognition for assisted living within smart homes” IEEE Transactions on Human-Machine Systems 47 3 368 379 2017 10.1109/THMS.2016.2641388 Search in Google Scholar

E. Ramanujam, T. Perumal, and S. Padmavathi, “Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review”, IEEE Sensors Journal, 21(12):13029–13040, 2021. RamanujamE. PerumalT. PadmavathiS. “Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review” IEEE Sensors Journal 21 12 13029 13040 2021 10.1109/JSEN.2021.3069927 Search in Google Scholar

S. Ranasinghe, F. Al Machot, and H. C. Mayr, “A review on applications of activity recognition systems with regard to performance and evaluation”, International Journal of Distributed Sensor Networks, 12(8):1550147716665520, 2016. RanasingheS. Al MachotF. MayrH. C. “A review on applications of activity recognition systems with regard to performance and evaluation” International Journal of Distributed Sensor Networks 12 8 1550147716665520, 2016 10.1177/1550147716665520 Search in Google Scholar

R. Riener, M. Rabuffetti, and C. Frigo, “Stair ascent and descent at different inclinations”, Gait & posture, 15(1):32–44, 2002. RienerR. RabuffettiM. FrigoC. “Stair ascent and descent at different inclinations” Gait & posture 15 1 32 44 2002 10.1016/S0966-6362(01)00162-X Search in Google Scholar

D. Rodríguez-Martín, A. Samà, C. Pérez-López, A. Català, and J. Cabestany, “Posture transition analysis with barometers: contribution to accelerometer based algorithms”, Neural Computing and Applications, 32:335–349, 2018. Rodríguez-MartínD. SamàA. Pérez-LópezC. CatalàA. CabestanyJ. “Posture transition analysis with barometers: contribution to accelerometer based algorithms” Neural Computing and Applications 32 335 349 2018 10.1007/s00521-018-3759-8 Search in Google Scholar

A. Sarabu and A. Santra, “Human action recognition in videos using convolution long short-term memory network with spatio-temporal networks”, Emerging Science Journal, 5:25–33, February 2021. SarabuA. SantraA. “Human action recognition in videos using convolution long short-term memory network with spatio-temporal networks” Emerging Science Journal 5 25 33 February 2021 10.28991/esj-2021-01254 Search in Google Scholar

A. B. Sargana, P. Angelov, and Z. Habib, Vision Based Human Activity Recognition: A Review, vol. 513, pp. 341–371. January 2017. SarganaA. B. AngelovP. HabibZ. Vision Based Human Activity Recognition: A Review 513 341 371 January 2017 10.1007/978-3-319-46562-3_23 Search in Google Scholar

S. Sharif, I. Murray, and G. Lee, “Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3d motion capture system”, Biomedical Engineering Letters, 8, May 2018. SharifS. MurrayI. LeeG. “Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3d motion capture system” Biomedical Engineering Letters 8 May 2018 10.1007/s13534-018-0072-5620854130603212 Search in Google Scholar

N. K. Suryadevara and S. C. Mukhopadhyay, “Assistive Technology for the Elderly”, Academic Press, 2020. SuryadevaraN. K. MukhopadhyayS. C. “Assistive Technology for the Elderly” Academic Press 2020 Search in Google Scholar

W. Tao, T. Liu, R. Zheng, and H. Feng, “Gait analysis using wearable sensors”, Sensors, 12(2):2255–2283, 2012. TaoW. LiuT. ZhengR. FengH. “Gait analysis using wearable sensors” Sensors 12 2 2255 2283 2012 10.3390/s120202255330416522438763 Search in Google Scholar

Tina, A. K. Sharma, S. Tomar, and K. Gupta, “Various approaches of human activity recognition: A review”, In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1668–1676, 2021. Tina SharmaA. K. TomarS. GuptaK. “Various approaches of human activity recognition: A review” In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) 1668 1676 2021 10.1109/ICCMC51019.2021.9418226 Search in Google Scholar

P. H. Truong, S. You, S.-H. Ji, and G.-M. Jeong, “Adaptive accumulation of plantar pressure for ambulatory activity recognition and pedestrian identification”, Sensors, 21:3842, June 2021. TruongP. H. YouS. JiS.-H. JeongG.-M. “Adaptive accumulation of plantar pressure for ambulatory activity recognition and pedestrian identification” Sensors 21 3842 June 2021 10.3390/s21113842819962834199381 Search in Google Scholar

M. Vrigkas, C. Nikou, and I. A. Kakadiaris, “A review of human activity recognition methods”, Frontiers in Robotics and AI, 2, 2015. VrigkasM. NikouC. KakadiarisI. A. “A review of human activity recognition methods” Frontiers in Robotics and AI 2 2015 10.3389/frobt.2015.00028 Search in Google Scholar

C. Wang, J. Z. Zhang, Z. Wang, and J. Wang, “Position-independent activity recognition model for smartphone based on frequency domain algorithm”, in Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, pp. 396–399, 2013. WangC. ZhangJ. Z. WangZ. WangJ. “Position-independent activity recognition model for smartphone based on frequency domain algorithm” in Proceedings of 2013 3rd International Conference on Computer Science and Network Technology 396 399 2013 10.1109/ICCSNT.2013.6967138 Search in Google Scholar

S. K. Yadav, K. Tiwari, H. M. Pandey, and S. Ali Akbar, “A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions”, Knowledge-Based Systems, 223:106970, 2021. YadavS. K. TiwariK. PandeyH. M. Ali AkbarS. “A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions” Knowledge-Based Systems 223 106970, 2021 10.1016/j.knosys.2021.106970 Search in Google Scholar

S. Yang, C. Li, X. Chen, Y. Zhao, H. Zhang, N. Wen, Z. Fan, and L. Pan, “Facile fabrication of high-performance pen ink-decorated textile strain sensors for human motion detection”, ACS Applied Materials & Interfaces, 12(17):19874–19881, 2020. PMID: 32253911 YangS. LiC. ChenX. ZhaoY. ZhangH. WenN. FanZ. PanL. “Facile fabrication of high-performance pen ink-decorated textile strain sensors for human motion detection” ACS Applied Materials & Interfaces 12 17 19874 19881 2020 PMID: 32253911 10.1021/acsami.9b2253432253911 Search in Google Scholar

S. Zhang, Y. Li, S. Zhang, F. Shahabi, S. Xia, Y. Deng, and N. Alshurafa, “Deep learning in human activity recognition withwearable sensors: A review on advances” Sensors, 22(4), Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland, February 2022. ZhangS. LiY. ZhangS. ShahabiF. XiaS. DengY. AlshurafaN. “Deep learning in human activity recognition withwearable sensors: A review on advances” Sensors 22 4 Publisher Copyright: © 2022 by the authors. Licensee MDPI Basel, Switzerland February 2022 10.3390/s22041476887904235214377 Search in Google Scholar

S. Zhang, Z. Wei, J. Nie, L. Huang, S. Wang, and Z. Li, “A review on human activity recognition using vision-based method”, Journal of Healthcare Engineering, 2017:1–31, July 2017. ZhangS. WeiZ. NieJ. HuangL. WangS. LiZ. “A review on human activity recognition using vision-based method” Journal of Healthcare Engineering 2017 1 31 July 2017 10.1155/2017/3090343554182429065585 Search in Google Scholar

Y. Zhao, J. Wang, Y. Zhang, H. Liu, Z. Chen, Y. Lu, Y. Dai, L. Xu, and S. Gao, “Flexible and wearable emg and psd sensors enabled locomotion mode recognition for ioht-based in-home rehabilitation”, IEEE Sensors Journal, 21(23):26311–26319, 2021. ZhaoY. WangJ. ZhangY. LiuH. ChenZ. LuY. DaiY. XuL. GaoS. “Flexible and wearable emg and psd sensors enabled locomotion mode recognition for ioht-based in-home rehabilitation” IEEE Sensors Journal 21 23 26311 26319 2021 10.1109/JSEN.2021.3058429 Search in Google Scholar

J. Zheng, H. Cao, D. Chen, R. Ansari, K.-C. Chu, and M.-C. Huang, “Designing deep reinforcement learning systems for musculoskeletal modeling and locomotion analysis using wearable sensor feedback”, IEEE Sensors Journal, 20(16):9274–9282, 2020. ZhengJ. CaoH. ChenD. AnsariR. ChuK.-C. HuangM.-C. “Designing deep reinforcement learning systems for musculoskeletal modeling and locomotion analysis using wearable sensor feedback” IEEE Sensors Journal 20 16 9274 9282 2020 10.1109/JSEN.2020.2986768 Search in Google Scholar

Z. Zhuang and Y. Xue, “Sport-related human activity detection and recognition using a smartwatch”, Sensors, 19(22):5001, Nov. 2019. ZhuangZ. XueY. “Sport-related human activity detection and recognition using a smartwatch” Sensors 19 22 5001 Nov. 2019 10.3390/s19225001689162231744127 Search in Google Scholar

eISSN:
1178-5608
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Engineering, Introductions and Overviews, other