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In-depth analysis of design & development for sensor-based human activity recognition system

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Human Activity Recognition (HAR) has gained much attention since sensor technology has become more advanced and cost-effective. HAR is a process of identifying the daily living activities of an individual with the help of an efficient learning algorithm and prospective user-generated datasets. This paper addresses the technical advancement and classification of HAR systems in detail. Design issues, future opportunities, recent state-of-the-art related works, and a generic framework for activity recognition are discussed in a comprehensive manner with analytical discussion. Different publicly available datasets with their features and incorporated sensors are also descr-processing techniques with various performance metrics like - Accuracy, F1-score, Precision, Recall, Computational times and evaluation schemes are discussed for the comprehensive understanding of the Activity Recognition Chain (ARC). Different learning algorithms are exploited and compared for learning-based performance comparison. For each specific module of this paper, a compendious number of references is also cited for easy referencing. The main aim of this study is to give the readers an easy hands-on implementation in the field of HAR with verifiable evidence of different design issues.

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  1. Romaissa B, Nini B, Sabokrou M, Hadid A (2020) Vision-based human activity recognition: a survey. Multimed Tools Appl 79(11)

  2. Mani N, Haridoss P, George B (2021) A wearable ultrasonic-based ankle angle and toe clearance sensing system for gait analysis. IEEE Sensors J 21(6):8593–8603

    Article  Google Scholar 

  3. Yuan G, Liu X, Yan Q, Qiao S, Wang Z, Yuan L (2021) Hand gesture recognition using deep feature fusion network based on wearable sensors. IEEE Sensors J 21(1):539–547

    Google Scholar 

  4. Amin Choudhury N, Moulik S, Choudhury S (2020) Cloud-based real-time and remote human activity recognition system using wearable sensors. IEEE Int Conf Consum Electron - Taiwan (ICCE-Taiwan), pp 1–2

  5. Moulik S, Majumdar S (2019) Fallsense: An automatic fall detection and alarm generation system in iot-enabled environment. IEEE Sensors J 19(19):8452–8459

    Article  Google Scholar 

  6. Jobanputra C, Bavishi J, Doshi N (2019) Human activity recognition: A survey. Procedia Comput Sci 155:698–703

    Article  Google Scholar 

  7. WHO, Physical activity. [Online]. Available:

  8. Wang A, Zhao S, Zheng C, Yang J, Chen G, Chang C-Y (2021) Activities of daily living recognition with binary environment sensors using deep learning: a comparative study. IEEE Sensors J 21(4):5423–5433

    Article  Google Scholar 

  9. Demrozi F, Pravadelli G, Bihorac A, Rashidi P (2020) Human activimty recognition using inertial, physiological and environmental sensors: a comprehensive survey. IEEE Access 8:210816–210836

    Article  Google Scholar 

  10. Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutors 15(3):1192–1209

    Article  Google Scholar 

  11. Slim SO, Atia A, Elfattah MM, Mostafa M-SM (2019) Survey on human activity recognition based on acceleration data. Int J Adv Comput Sci Appl 10(3)

  12. Tian Y, Wang X, Chen L, Liu Z (2019) Wearable sensor-based human activity recognition via two-layer diversity-enhanced multiclassifier recognition method. Sensors, 19(9)

  13. Zhang B, Zheng R, Liu J (2021) A multi-source unsupervised domain adaptation method for wearable sensor based human activity recognition: Poster abstract. In: Proceedings of the 20th international conference on information processing in sensor networks (co-located with CPS-IoT Week 2021), ser IPSN ’21. Association for Computing Machinery, New York, NY, USA, pp 410–411

  14. Wang H, Zhao J, Li J, Tian L, Tu P, Cao T, An Y, Wang K, Li S (2020) Wearable sensor-based human activity recognition using hybrid deep learning techniques. Secur Commun Netw 2020:2132138

    Article  Google Scholar 

  15. Mekruksavanich S, Jitpattanakul A (2021) Deep convolutional neural network with rnns for complex activity recognition using wrist-worn wearable sensor data. Electron 10(14)

  16. Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y (2015) Physical human activity recognition using wearable sensors. Sensors 15(12):31314–31338

    Article  Google Scholar 

  17. Du Y, Lim Y, Tan Y (2019) A novel human activity recognition and prediction in smart home based on interaction. Sensors 19(20)

  18. Irvine N, Nugent C, Zhang S, Wang H, NG WWY (2020) Neural network ensembles for sensor-based human activity recognition within smart environments. Sensors 20(1)

  19. Tax N (2018) Human activity prediction in smart home environments with lstm neural networks. In: 2018 14th international conference on intelligent environments (IE), pp 40–47

  20. Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Futur Gener Comput Syst 81:307–313

    Article  Google Scholar 

  21. Andrade-Ambriz YA, Ledesma S, Ibarra-Manzano M-A, Oros-Flores MI, Almanza-Ojeda D-L (2022) Human activity recognition using temporal convolutional neural network architecture. Expert Syst Appl 191:116287

    Article  Google Scholar 

  22. Jindal S, Sachdeva M, Kushwaha AKS (2022) Deep learning for video based human activity recognition: Review and recent developments. In: Bansal RC, Zemmari A, Sharma KG, Gajrani J (eds) Proceedings of international conference on computational intelligence and emerging power system. Springer, Singapore, pp 71–83

  23. Ehatisham-Ul-Haq M, Javed A, Azam MA, Malik HMA, Irtaza A, Lee IH, Mahmood MT (2019) Robust human activity recognition using multimodal feature-level fusion. IEEE Access 7:60736–60751

    Article  Google Scholar 

  24. Mliki H, Bouhlel F, Hammami M (2020) Human activity recognition from uav-captured video sequences. Pattern Recognit 100:107140

    Article  Google Scholar 

  25. Ke S-R, Thuc HLU, Lee Y-J, Hwang J-N, Yoo J-H, Choi K-H (2013) A review on video-based human activity recognition. Comput 2(2):88–131

    Article  Google Scholar 

  26. Kang J, Shin J, Shin J, Lee D, Choi A (2022) Robust human activity recognition by integrating image and accelerometer sensor data using deep fusion network. Sensors 22(1)

  27. Ni J, Sarbajna R, Liu Y, Ngu AHH, Yan Y (2021) Cross-modal knowledge distillation for vision-to-sensor action recognition

  28. Banjarey K, Sahu SP, Dewangan DK (2022) Human activity recognition using 1d convolutional neural network. In: Shakya S, Balas VE, Kamolphiwong S, Du K-L (eds) Sentimental analysis and deep learning. Springer Singapore, Singapore, pp 691–702

    Chapter  Google Scholar 

  29. Vyas R, Doddabasappla K (2022) Fft spectrum spread with machine learning (ml) analysis of triaxial acceleration from shirt pocket and torso for sensing coughs while walking. IEEE Sensors Lett 6(1):1–4

    Article  Google Scholar 

  30. Choudhury NA, Moulik S, Roy DS (2021) Physique-based human activity recognition using ensemble learning and smartphone sensors. IEEE Sensors J 21(15):16852–16860

    Article  Google Scholar 

  31. Nandy A, Saha J, Chowdhury C, Singh KPD (2019) Detailed human activity recognition using wearable sensor and smartphones, In: International conference on opto-electronics and applied optics (Optronix), pp 1–6

  32. Asim Y, Azam MA, Ehatisham-ul Haq M, Naeem U, Khalid A (2020) Context-aware human activity recognition (cahar) in-the-wild using smartphone accelerometer. IEEE Sensors J 20(8):4361–4371

    Article  Google Scholar 

  33. Ignatov A (2018) Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput 62:915–922

    Article  Google Scholar 

  34. Chen D, Yongchareon S, Lai EM-K, Sheng, QZ Liesaputra V (2021) Locally-weighted ensemble detection-based adaptive random forest classifier for sensor-based online activity recognition for multiple residents. IEEE Internet Things J 1–1

  35. Yu H, Chen Z, Zhang X, Chen X, Zhuang F, Xiong H, Cheng X (2021) Fedhar: Semi-supervised online learning for personalized federated human activity recognition. IEEE Trans Mob Comput 1–1

  36. Vakili M, Rezaei M (2021) Incremental learning techniques for online human activity recognition

  37. Abdul Haroon PS, Premachand DR (2021) Human activity recognition using machine learning approach. J Robot Control (JRC) 2(5):395–399

    Google Scholar 

  38. Biswal A, Nanda S, Panigrahi CR, Cowlessur SK, Pati B (2021) Human activity recognition using machine learning: a review. In: Panigrahi CR, Pati B, Pattanayak BK, Amic S, Li K-C (eds) Progress in advanced computing and intelligent engineering. Springer Singapore, Singapore, pp 323–333

  39. Papaleonidas A, Psathas AP, Iliadis L (2021) High accuracy human activity recognition using machine learning and wearable devices’ raw signals. J Inf Telecommun 0(0):1–17

  40. Subasi A, Khateeb K, Brahimi T, Sarirete A (2020) Chapter 5 - human activity recognition using machine learning methods in a smart healthcare environment. In: Lytras MD, Sarirete A (eds) Innovation in Health Informatics. ser. Next Gen Tech Driven Personalized Med &Smart Healthcare. Academic Press, pp 123–144

  41. Thakur D, Guzzo A, Fortino G (2021) t-sne and pca in ensemble learning based human activity recognition with smartwatch*. In: 2021 IEEE 2nd International conference on human-machine systems (ICHMS), pp 1–6

  42. Sekiguchi R, Abe K, Yokoyama T, Kumano M, Kawakatsu M (2020) Ensemble learning for human activity recognition. In: Adjunct Proceedings of the 2020 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2020 ACM international symposium on wearable computers. ser. UbiComp-ISWC ’20. Association for Computing Machinery, New York, NY, USA, p 335–339

  43. Kasubi JW, Huchaiah MD (2021) Human activity recognition for multi-label classification in smart homes using ensemble methods. In: Solanki A, Sharma SK, Tarar S, Tomar P, Sharma S, Nayyar A (eds) Artificial intelligence and sustainable computing for Smart City. Springer International Publishing, Cham, pp 282–294

    Chapter  Google Scholar 

  44. Wan S, Qi L, Xu X, Tong C, Gu Z (2020) Deep learning models for real-time human activity recognition with smartphones. Mob Netw Appl 25(2):743–755

    Article  Google Scholar 

  45. Xia K, Huang J, Wang H (2020) Lstm-cnn architecture for human activity recognition. IEEE Access 8:56855–56866

    Article  Google Scholar 

  46. Mekruksavanich S, Jitpattanakul A (2021) LSTM networks using smartphone data for sensor-based human activity recognition in smart homes. Sensors 21(5)

  47. Chinimilli PT, Redkar S, Zhang W (2017) Human activity recognition using inertial measurement units and smart shoes. In: 2017 American control conference (ACC), pp 1462–1467

  48. Vu CC, Kim J (2018) Human motion recognition using swcnt textile sensor and fuzzy inference system based smart wearable. Sensors and Actuators A: Physical 283:263–272

    Article  Google Scholar 

  49. Lara OD, Labrador MA (2012) A mobile platform for real-time human activity recognition. In: 2012 IEEE consumer communications and networking conference (CCNC), pp 667–671

  50. Jain Y, Tang CI, Min C, Kawsar F, Mathur A (2022) Collossl: Collaborative self-supervised learning for human activity recognition. Proc ACM Interact Mob Wearable Ubiquitous Technol 6(1)

  51. Lattanzi E, Donati M, Freschi V (2022) Exploring artificial neural networks efficiency in tiny wearable devices for human activity recognition. Sensors 22(7)

  52. Xu G, Wan Q, Deng W, Guo T, Cheng J (2022) Smart-sleeve: a wearable textile pressure sensor array for human activity recognition. Sensors 22(5)

  53. Ghosal S, Sarkar M, Sarkar R (2022) NoFED-Net: Non-Linear Fuzzy Ensemble of Deep Neural Networks for Human Activity Recognition. IEEE Internet Things J 1-1

  54. Rashid N, Demirel BU, Faruque MAA (2022) Ahar: Adaptive cnn for energy-efficient human activity recognition in low-power edge devices. IEEE Internet Things J 1–1

  55. Han C, Zhang L, Tang Y, Huang W, Min F, He J (2022) Human activity recognition using wearable sensors by heterogeneous convolutional neural networks. Expert Syst Appl 198:116764

    Article  Google Scholar 

  56. Alsaify BA, Almazari MM, Alazrai R, Alouneh S, Daoud MI (2022) A csi-based multi-environment human activity recognition framework. Appl Sci 12(2)

  57. Kwon E, Park H, Byon S, Jung E, Lee Y (2018) HaaS(human activity analytics as a service) using sensor data of smart devices. In: 2018 International conference on information and communication technology convergence (ICTC), pp 1500–1502

  58. Dehzangi O, Sahu V (2018) Imu-based robust human activity recognition using feature analysis, extraction, and reduction, In: 2018 24th International conference on pattern recognition (ICPR), pp 1402–1407

  59. Patel AD, Shah JH (2019) Performance analysis of supervised machine learning algorithms to recognize human activity in ambient assisted living environment. In: 2019 IEEE 16th India council international conference (INDICON), pp 1–4

  60. Khokhlov I, Reznik L, Cappos J, Bhaskar R (2018) Design of activity recognition systems with wearable sensors. In: 2018 IEEE sensors applications symposium (SAS), pp 1–6

  61. Hong Y, Kim I, Ahn SC, Kim H (2008) Activity recognition using wearable sensors for elder care. In: 2008 Second international conference on future generation communication and networking, vol 2, pp 302–305

  62. Choudhury NA, Soni B (2023) An adaptive batch size based-cnn-lstm framework for human activity recognition in uncontrolled environment. IEEE Trans Ind Inform 1–9

  63. Fu Z, He X, Wang E, Huo J, Huang J, Wu D (2021) Personalized human activity recognition based on integrated wearable sensor and transfer learning. Sensors 21(3)

  64. Mekruksavanich S, Jitpattanakul A (2021) Biometric user identification based on human activity recognition using wearable sensors: An experiment using deep learning models. Electron 10(3)

  65. Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2013) A public domain dataset for human activity recognition using smartphones. In: ESANN

  66. Zhang M, Sawchuk A (2012) Usc-had: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: ACM international conference on ubiquitous computing (Ubicomp) workshop on situation, activity and goal awareness (SAGAware), vol 09, pp 1036–1043

  67. Damaševičius R, Maskeliūnas R, Venčkauskas A, Woźniak M (2016) Smartphone user identity verification using gait characteristics. Symmetry 8(10)

  68. Yang P, Yang C, Lanfranchi V, Ciravegna F (2022) Activity graph based convolutional neural network for physical activity recognition using acceleration and gyroscope data. IEEE Trans Ind Inform 1–1

  69. Ehatisham-ul-Haq M, Murtaza F, Azam MA, Amin Y (2022) Daily living activity recognition in-the-wild: modeling and inferring activity-aware human contexts. Electronics 11(2)

  70. Siirtola P, Röning J (2021) Context-aware incremental learning-based method for personalized human activity recognition. J Ambient Intell Humanized Comput 12(12):10499–10513

    Article  Google Scholar 

  71. Choudhury NA, Moulik S, Roy DS (2021) Harsense: statistical human activity recognition dataset

  72. Malekzadeh M, Clegg RG, Cavallaro A, Haddadi H (2019) Mobile sensor data anonymization. In: Proceedings of the international conference on internet of things design and implementation, ser. IoTDI ’19. Assoc Comput Mach, New York, NY, USA , pp 49–58

  73. Vavoulas G, Chatzaki C, Malliotakis T, Pediaditis M, Tsiknakis M (2016) The mobiact dataset: recognition of activities of daily living using smartphones. In: 2nd international conference on information and communication technologies for ageing well and e-health, vol 01, pp 143–151

  74. Chavarriaga R, Sagha H, Calatroni A, Digumarti ST, Tröster G, J. Millán del R, Roggen D (2013) The opportunity challenge: a benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters, 34(15):2033–2042, smart Approaches for Human Action Recognition

  75. Banos O, Villalonga C, Garcia R, Saez A, Damas M, Holgado-Terriza JA, Lee S, Pomares H, Rojas I (2015) Design, implementation and validation of a novel open framework for agile development of mobile health applications. BioMedical Engineering OnLine 14(2):S6

  76. Sztyler T, Stuckenschmidt H (2016) On-body localization of wearable devices: an investigation of position-aware activity recognition. In: 2016 IEEE international conference on pervasive computing and communications (PerCom), pp 1–9

  77. Hayashi T, Nishida M, Kitaoka N, Takeda K (2015) Daily activity recognition based on dnn using environmental sound and acceleration signals. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp 2306–2310

  78. Kwapisz JR, Weiss GM, Moore SA (2011) Activity recognition using cell phone accelerometers. SIGKDD Explor Newsl 12(2):74–82

    Article  Google Scholar 

  79. Reiss A, Stricker D (2012) Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th international symposium on wearable computers, vol 06

  80. Reyes-Ortiz J-L, Oneto L, Ghio A, Samá Monsonís A, Anguita D, Parra F (2014) Human activity recognition on smartphones with awareness of basic activities and postural transitions. In: Artificial neural networks and machine learning - ICANN 2014. Springer International Publishing, Cham, pp 177–184

  81. Bhat G, Tran N, Shill H, Ogras UY (2020) w-HAR: an activity recognition dataset and framework using low-power wearable devices. Sensors 20(18)

  82. Stisen A, Blunck H, Bhattacharya S, Prentow T, Kjærgaard M, Dey A, Sonne T, Jensen M (2015) Smart devices are different: assessing and mitigating mobile sensing heterogeneities for activity recognition. In: Proceedings of the 13th ACM conference on embedded networked sensor systems, ser. SenSys ’15. Assoc Comput Mach, New York, NY, USA, pp 127–140

  83. Khan IU, Afzal S, Lee JW (2022) Human activity recognition via hybrid deep learning based model. Sensors 22(1)

  84. Mutegeki R, Han DS (2020) A cnn-lstm approach to human activity recognition, In: 2020 international conference on artificial intelligence in information and communication (ICAIIC), pp 362–366

  85. Abdel-Basset M, Hawash H, Chakrabortty RK, Ryan M, Elhoseny M, Song H (2021) St-deephar: Deep learning model for human activity recognition in ioht applications. IEEE Internet of Things J 8(6):4969–4979

    Article  Google Scholar 

  86. Cruciani F, Vafeiadis A, Nugent C, Cleland I, McCullagh P, Votis K, Giakoumis D, Tzovaras D, Chen L, Hamzaoui R (2020) Feature learning for human activity recognition using convolutional neural networks. CCF Trans Pervasive Comput Interact 2(1):18–32

    Article  Google Scholar 

  87. Dua N, Singh S, Semwal V (2021) Multi-input cnn-gru based human activity recognition using wearable sensors. Comput 103:1–18

    Article  Google Scholar 

  88. Kwon H, Tong C, Haresamudram H, Gao Y, Abowd GD, Lane ND, Plötz T (2020) Imutube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition. Proc ACM Interact Mob Wearable Ubiquitous Technol 4(3)

  89. Garcia-Gonzalez D, Rivero D, Fernandez-Blanco E, Luaces MR (2023) New machine learning approaches for real-life human activity recognition using smartphone sensor-based data. Knowledge-Based Syst 262:110260

    Article  Google Scholar 

  90. Panja AK, Rayala A, Agarwala A, Neogy S, Chowdhury C (2023) A hybrid tuple selection pipeline for smartphone based human activity recognition. Expert Syst Appl 217:119536

    Article  Google Scholar 

  91. Yadav SK, Sai S, Gundewar A, Rathore H, Tiwari K, Pandey HM, Mathur M (2022) CSITime: privacy-preserving human activity recognition using WiFi channel state information. Neural Networks 146:11–21

    Article  Google Scholar 

  92. Ismail Fawaz H, Lucas B, Forestier G, Pelletier C, Schmidt DF, Weber J, Webb GI, Idoumghar L, Muller P-A, Petitjean F (2020) Inceptiontime: finding alexnet for time series classification. Data Min Knowl Discov 34(6):1936–1962

    Article  MathSciNet  Google Scholar 

  93. Rashid N, Demirel BU, Faruque MAA (2022) Ahar: adaptive cnn for energy-efficient human activity recognition in low-power edge devices. IEEE Internet Things J 1–1

  94. Luo F, Khan S, Huang Y, Wu K (2021) Binarized neural network for edge intelligence of sensor-based human activity recognition. IEEE Trans Mob Comput 1–1

  95. Taylor W, Shah SA, Dashtipour K, Zahid A, Abbasi QH, Imran MA (2020) An intelligent non-invasive real-time human activity recognition system for next-generation healthcare. Sensors 20(9)

  96. Guo Y, Chu Y, Jiao B, Cheng J, Yu Z, Cui N, Ma L (2021) Evolutionary dual-ensemble class imbalance learning for human activity recognition. IEEE Trans Emerg Top Comput Intell 1–12

  97. Khaled H, Abu-Elnasr O, Elmougy S, Tolba AS (2021) Intelligent system for human activity recognition in iot environment. Complex Intell Syst

  98. Rahman A, Hassan I, Ahad MAR (2021) Nurse care activity recognition: a cost-sensitive ensemble approach to handle imbalanced class problem in the wild. Assoc Comput Mach, New York, NY, USA, pp 440–445

    Google Scholar 

  99. Hamad RA, Yang L, Woo WL, Wei B (2020) Joint learning of temporal models to handle imbalanced data for human activity recognition. Appl Sci 10(15)

  100. Choudhury NA, Soni B (2022) Effect of shallow and ensemble learning models for human activity recognition in uncontrolled environment. In: 2022 IEEE 19th India council international conference (INDICON), pp 1–6

  101. Dua N, Singh SN, Semwal VB, Challa SK (2023) Inception inspired CNN-GRU hybrid network for human activity recognition. Multimed Tools Appl 82(4):5369–5403

    Article  Google Scholar 

  102. Liu K, Liu W, Ma H, Tan M, Gan C (2021) A real-time action representation with temporal encoding and deep compression. IEEE Trans Circ Syst Vid Technol 31(2):647–660

    Article  Google Scholar 

  103. Choudhury NA, Soni B (2022) An efficient cnn-lstm approach for smartphone sensor-based human activity recognition system. In: 2022 5th International conference on computational intelligence and networks (CINE), pp 01–06

  104. Li X, He Y, Fioranelli F, Jing X (2021) Semisupervised human activity recognition with radar micro-doppler signatures. IEEE Trans Geosci Remote Sens 1–12

  105. Tang CI, Perez-Pozuelo I, Spathis D, Brage S, Wareham NJ, Mascolo C (2021) SelfHAR: improving human activity recognition through self-training with unlabeled data. CoRR arXiv:2102.06073

  106. Hassan I, Mursalin A, Salam, RB, Sakib N, Haque HMZ (2021) AutoAct: an auto labeling approach based on activities of daily living in the wild domain. In: 2021 joint 10th international conference on informatics, electronics vision (ICIEV) and 2021 5th international conference on imaging, vision pattern recognition (icIVPR), pp 1–8

  107. Chen X, Liu W, Liu X, Zhang Y, Han J, Mei T (2022) Maple: Masked pseudo-labeling autoencoder for semi-supervised point cloud action recognition, In: Proceedings of the 30th ACM international conference on multimedia, ser. MM ’22. Assoc Comput Mach, New York, NY, USA, pp 708–718

  108. Liu K, Liu W, Gan C, Tan M, Ma H (2018) T-C3D: Temporal convolutional 3d network for real-time action recognition. Proceedings of the AAAI Conference on Artificial Intelligence 32(1)

  109. Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C (Appl Revi) 42(6):790–808

    Article  Google Scholar 

  110. Wang J, Chen Y, Hao S, Peng X, Hu L (2019) Deep learning for sensor-based activity recognition: a survey. Pattern recognition letters, deep learning for. Pattern Recognit 119:3–11

  111. Sun Z, Ke Q, Rahmani H, Bennamoun M, Wang G, Liu J (2023) Human action recognition from various data modalities: A review. IEEE Trans Pattern Anal Mach Intell 45(3):3200–3225

    Google Scholar 

  112. Ramanujam E, Perumal T, Padmavathi S (2021) Human activity recognition with smartphone and wearable sensors using deep learning techniques: a review. IEEE Sensors J 21(12):13029–13040

    Article  Google Scholar 

  113. Awan MA, Guangbin Z, Kim H-C, Kim S-D (2015) Subject-independent human activity recognition using smartphone accelerometer with cloud support. Int J Ad Hoc Ubiquit Comput 20(3):172–185

    Article  Google Scholar 

  114. Hoang ML, Pietrosanto A (2021) A new technique on vibration optimization of industrial inclinometer for mems accelerometer without sensor fusion. IEEE Access 9:20295–20304

    Article  Google Scholar 

  115. Park S, Gil M-S, Im H, Moon Y-S (2019) Measurement noise recommendation for efficient kalman filtering over a large amount of sensor data. Sensors 19(5)

  116. Chen Y, Li D, Li Y, Ma X, Wei J (2017) Use moving average filter to reduce noises in wearable ppg during continuous monitoring. In: Giokas K, Bokor L, Hopfgartner F (eds) eHealth 360°. Springer International Publishing, Cham, pp 193–203

  117. Jadhav A, Pramod D, Ramanathan K (2019) Comparison of performance of data imputation methods for numeric dataset. Appl Artif Intell 33(10):913–933

    Article  Google Scholar 

  118. McKinney W (2010) Data structures for statistical computing in python. In: van der Walt S, Millman J (eds) Proceedings of the 9th python in science conference, pp 56–61

  119. Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R, Picus M, Hoyer S, van Kerkwijk MH, Brett M, Haldane A, del Río JF, Wiebe M, Peterson P, Gérard-Marchant P, Sheppard K, Reddy T, Weckesser W, Abbasi H, Gohlke C, Oliphant TE (2020) Array programming with NumPy. Nature 585(7825):357–362

    Article  Google Scholar 

  120. Bagozi A, Bianchini D, De Antonellis V, Garda M, Marini A (2019) A relevance-based approach for big data exploration. Futur Gene Comput Syst 101:51–69

    Article  Google Scholar 

  121. Midway SR (2020) Principles of effective data visualization. Patterns 1(9):100141

    Google Scholar 

  122. Wang H, Bah MJ, Hammad M (2019) Progress in outlier detection techniques: a survey. IEEE Access 7:107964–108000

    Article  Google Scholar 

  123. Suto J, Oniga S, Sitar PP (2016) Feature analysis to human activity recognition. Int J Comput Commun Control 12(1):116–130

    Article  Google Scholar 

  124. Yala N, Fergani B, Fleury A (2015) Feature extraction for human activity recognition on streaming data. In: 2015 International symposium on innovations in intelligent systems and applications (INISTA), pp 1–6

  125. Al Machot F, Mayr HC (2016) Improving human activity recognition by smart windowing and spatio-temporal feature analysis. In: Proceedings of the 9th ACM international conference on pervasive technologies related to assistive environments. ser. PETRA ’16. Association for Computing Machinery, New York, NY, USA

  126. Chen Z, Zhang L, Cao Z, Guo J (2018) Distilling the knowledge from handcrafted features for human activity recognition. IEEE Trans Ind Inform 14(10):4334–4342

    Article  Google Scholar 

  127. Berrar D (2019) Cross-validation. In: Ranganathan S, Gribskov M, Nakai K, Schönbach C (eds) Encyclopedia of bioinformatics and computational biology. Academic Press, Oxford, pp 542–545

    Chapter  Google Scholar 

  128. Ramezan CA, Warner TA, Maxwell AE (2019) Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens 11(2)

  129. Schaffer C (1994) Selecting a classification method by cross-validation. Machine Learning 13

  130. Wong T-T, Yeh P-Y (2020) Reliable accuracy estimates from k-fold cross validation. IEEE Trans Knowl Data Eng 32(8):1586–1594

    Article  Google Scholar 

  131. Yadav S, Shukla S (2016) Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: IEEE 6th international conference on advanced computing (IACC), pp 78–83

  132. Wong T-T (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit 48(9):2839–2846

    Article  MATH  Google Scholar 

  133. Kearns M, Ron D (1999) Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput 11(6):1427–1453

    Article  Google Scholar 

  134. Xu Q-S, Liang Y-Z (2001) Monte carlo cross validation. Chemometr Intell Lab Syst 56(1):1–11

    Article  Google Scholar 

  135. Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and smote. Inf Sci 465:1–20

    Article  Google Scholar 

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Correspondence to Nurul Amin Choudhury.

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Nurul Amin Choudhury and Badal Soni contributed equally to this work.

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Choudhury, N.A., Soni, B. In-depth analysis of design & development for sensor-based human activity recognition system. Multimed Tools Appl (2023).

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