Deep Learning Algorithms for Human Activity Recognition in Manual Material Handling Tasks
Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse...
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| Published in: | Sensors (Basel, Switzerland) Vol. 25; no. 21; p. 6705 |
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| Language: | English |
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02.11.2025
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| Abstract | Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors’ data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70–30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70–30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires ×57.1 and ×31.3 more Multiply and ACcumulate (MAC) and ×100.8 and ×28.3 more Multiplication and Addition (MA) operations, which measure the complexity of the network’s inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. |
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| AbstractList | Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers' health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors' data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70-30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70-30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires ×57.1 and ×31.3 more Multiply and ACcumulate (MAC) and ×100.8 and ×28.3 more Multiplication and Addition (MA) operations, which measure the complexity of the network's inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers' health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors' data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70-30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70-30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires ×57.1 and ×31.3 more Multiply and ACcumulate (MAC) and ×100.8 and ×28.3 more Multiplication and Addition (MA) operations, which measure the complexity of the network's inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH.Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers' health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors' data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70-30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70-30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires ×57.1 and ×31.3 more Multiply and ACcumulate (MAC) and ×100.8 and ×28.3 more Multiplication and Addition (MA) operations, which measure the complexity of the network's inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. |
| Audience | Academic |
| Author | Avizzano, Carlo Alberto Filippeschi, Alessandro Bassani, Giulia |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41228930$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1145/1390156.1390294 10.1109/JIOT.2018.2823084 10.1145/3090076 10.3390/s20061557 10.1109/JAS.2024.124329 10.3390/s16020189 10.1109/SSCI.2017.8280908 10.1038/s41598-024-57912-3 10.3390/s151229858 10.1109/LRA.2019.2894389 10.1109/INDIN51773.2022.9976078 10.1145/2499621 10.3390/s25134028 10.1007/s11831-023-09986-x 10.1109/PerCom59722.2024.10494448 10.1016/j.apergo.2022.103693 10.1145/3134230.3134231 10.1016/j.eswa.2018.03.056 10.1007/s11042-023-15830-y 10.1109/JLT.2024.3386886 10.1109/IoTaIS60147.2023.10346052 10.1109/JSEN.2023.3338264 10.1016/j.future.2024.06.016 10.1016/j.rcim.2022.102449 10.1109/78.650093 10.3390/s20143877 10.1016/j.apergo.2021.103386 10.1016/j.eswa.2024.123143 10.1016/j.engappai.2024.107850 10.1109/MED51440.2021.9480225 10.1109/PerCom53586.2022.9762388 10.1016/j.compbiomed.2022.106060 10.1115/1.4039145 10.3390/s16010115 10.1109/TNNLS.2016.2582924 10.1371/journal.pone.0192938 10.3390/s22041476 10.1016/j.bspc.2024.106870 10.3115/v1/D14-1179 10.1016/j.neunet.2018.07.011 10.3390/s17112556 10.3390/machines13080669 10.1016/j.inffus.2021.11.006 10.1109/TCDS.2023.3326192 10.3390/s25185765 10.1016/j.protcy.2016.05.114 10.3390/s23042182 10.1016/j.bspc.2019.02.011 10.1007/s12530-022-09480-y 10.1108/IR-04-2021-0077 10.3390/s19040947 10.1109/ACCESS.2022.3140373 10.1007/s12062-020-09260-z 10.1109/ACCESS.2020.2982225 10.1007/978-3-319-13817-6_11 10.1142/S0218488598000094 10.3390/s21030777 10.1109/JSEN.2021.3113123 10.3390/s22010134 10.1109/JBHI.2019.2909688 |
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| References | Syed (ref_32) 2020; 11 Huang (ref_63) 2019; 24 Xia (ref_51) 2020; 8 Maurice (ref_3) 2019; 4 ref_14 ref_13 ref_12 ref_55 ref_54 Schrader (ref_2) 2020; 13 Dentamaro (ref_29) 2024; 246 ref_53 ref_52 Greff (ref_39) 2016; 28 ref_19 Trkov (ref_31) 2022; 101 ref_15 Zhang (ref_25) 2023; 14 Qiu (ref_57) 2022; 80 Bulling (ref_18) 2014; 46 ref_61 Gu (ref_47) 2018; 5 ref_60 Almaslukh (ref_45) 2017; 17 Syed (ref_50) 2020; 11 ref_22 Zhang (ref_28) 2024; 73 ref_66 ref_65 ref_20 ref_64 Freire (ref_62) 2024; 25 Imran (ref_24) 2023; 24 Losey (ref_56) 2018; 70 Inkulu (ref_67) 2022; 49 Azizi (ref_5) 2018; 4 ref_26 Moutinho (ref_68) 2023; 80 Dahou (ref_27) 2024; 160 Chen (ref_9) 2021; 54 Luzheng (ref_11) 2019; 51 Thakur (ref_23) 2022; 10 Ponce (ref_4) 2019; 15 ref_36 ref_35 Attal (ref_17) 2015; 15 Kumar (ref_33) 2024; 31 Xiong (ref_16) 2024; 11 ref_38 Khosravy (ref_69) 2024; 16 Porta (ref_42) 2021; 93 Nweke (ref_10) 2018; 105 ref_46 Bassani (ref_34) 2021; 21 ref_44 ref_43 ref_41 Guan (ref_58) 2017; 1 ref_1 Hochreiter (ref_37) 1998; 6 Rajesh (ref_7) 2016; 24 Benmessabih (ref_30) 2024; 131 Buda (ref_59) 2018; 106 ref_49 ref_48 ref_8 Kaya (ref_21) 2024; 83 ref_6 Schuster (ref_40) 1997; 45 |
| References_xml | – ident: ref_46 doi: 10.1145/1390156.1390294 – volume: 5 start-page: 2085 year: 2018 ident: ref_47 article-title: Locomotion activity recognition using stacked denoising autoencoders publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2823084 – volume: 54 start-page: 1 year: 2021 ident: ref_9 article-title: Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities publication-title: ACM Comput. Surv. (CSUR) – volume: 1 start-page: 1 year: 2017 ident: ref_58 article-title: Ensembles of deep lstm learners for activity recognition using wearables publication-title: Interact. Mob. Wearable Ubiquitous Technol. doi: 10.1145/3090076 – ident: ref_6 doi: 10.3390/s20061557 – volume: 11 start-page: 1075 year: 2024 ident: ref_16 article-title: Intuitive human-robot-environment interaction with EMG signals: A review publication-title: IEEE/CAA J. Autom. Sin. doi: 10.1109/JAS.2024.124329 – ident: ref_44 doi: 10.3390/s16020189 – ident: ref_12 doi: 10.1109/SSCI.2017.8280908 – ident: ref_26 doi: 10.1038/s41598-024-57912-3 – ident: ref_1 – volume: 15 start-page: 31314 year: 2015 ident: ref_17 article-title: Physical human activity recognition using wearable sensors publication-title: Sensors doi: 10.3390/s151229858 – volume: 4 start-page: 1132 year: 2019 ident: ref_3 article-title: Activity recognition for ergonomics assessment of industrial tasks with automatic feature selection publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2019.2894389 – ident: ref_65 doi: 10.1109/INDIN51773.2022.9976078 – volume: 46 start-page: 1 year: 2014 ident: ref_18 article-title: A tutorial on human activity recognition using body-worn inertial sensors publication-title: ACM Comput. Surv. (CSUR) doi: 10.1145/2499621 – ident: ref_20 doi: 10.3390/s25134028 – volume: 31 start-page: 179 year: 2024 ident: ref_33 article-title: Human activity recognition (har) using deep learning: Review, methodologies, progress and future research directions publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-023-09986-x – ident: ref_36 doi: 10.1109/PerCom59722.2024.10494448 – volume: 4 start-page: 1 year: 2018 ident: ref_5 article-title: Design and fabrication of intelligent material handling system in modern manufacturing with industry 4.0 approaches publication-title: Int. Robot. Autom. J. – volume: 101 start-page: 103693 year: 2022 ident: ref_31 article-title: Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles publication-title: Appl. Ergon. doi: 10.1016/j.apergo.2022.103693 – volume: 73 start-page: 1 year: 2024 ident: ref_28 article-title: Multi-STMT: Multi-level network for human activity recognition based on wearable sensors publication-title: IEEE Trans. Instrum. Meas. – ident: ref_61 doi: 10.1145/3134230.3134231 – volume: 105 start-page: 233 year: 2018 ident: ref_10 article-title: Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.03.056 – volume: 83 start-page: 10815 year: 2024 ident: ref_21 article-title: Human activity recognition from multiple sensors data using deep CNNs publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-023-15830-y – volume: 25 start-page: 4177 year: 2024 ident: ref_62 article-title: Computational complexity optimization of neural network-based equalizers in digital signal processing: A comprehensive approach publication-title: J. Light. Technol. doi: 10.1109/JLT.2024.3386886 – ident: ref_43 doi: 10.1109/IoTaIS60147.2023.10346052 – volume: 24 start-page: 1963 year: 2023 ident: ref_24 article-title: Smart-wearable sensors and cnn-bigru model: A powerful combination for human activity recognition publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2023.3338264 – volume: 160 start-page: 375 year: 2024 ident: ref_27 article-title: TCN-inception: Temporal convolutional network and inception modules for sensor-based human activity recognition publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2024.06.016 – volume: 11 start-page: 597 year: 2020 ident: ref_50 article-title: Continuous human activity recognition in logistics from inertial sensor data using temporal convolutions in CNN publication-title: Int. J. Adv. Comput. Sci. Appl. – volume: 80 start-page: 102449 year: 2023 ident: ref_68 article-title: Deep learning-based human action recognition to leverage context awareness in collaborative assembly publication-title: Robot. Comput.-Integr. Manuf. doi: 10.1016/j.rcim.2022.102449 – volume: 45 start-page: 2673 year: 1997 ident: ref_40 article-title: Bidirectional recurrent neural networks publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.650093 – ident: ref_14 doi: 10.3390/s20143877 – volume: 93 start-page: 103386 year: 2021 ident: ref_42 article-title: Classifying diverse manual material handling tasks using a single wearable sensor publication-title: Appl. Ergon. doi: 10.1016/j.apergo.2021.103386 – volume: 246 start-page: 123143 year: 2024 ident: ref_29 article-title: Human activity recognition with smartphone-integrated sensors: A survey publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2024.123143 – volume: 131 start-page: 107850 year: 2024 ident: ref_30 article-title: Online human motion analysis in industrial context: A review publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2024.107850 – ident: ref_55 doi: 10.1109/MED51440.2021.9480225 – ident: ref_64 doi: 10.1109/PerCom53586.2022.9762388 – ident: ref_48 doi: 10.1016/j.compbiomed.2022.106060 – volume: 70 start-page: 010804 year: 2018 ident: ref_56 article-title: A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction publication-title: Appl. Mech. Rev. doi: 10.1115/1.4039145 – ident: ref_35 doi: 10.3390/s16010115 – volume: 28 start-page: 2222 year: 2016 ident: ref_39 article-title: LSTM: A search space odyssey publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2016.2582924 – ident: ref_13 doi: 10.1371/journal.pone.0192938 – ident: ref_19 doi: 10.3390/s22041476 – ident: ref_22 doi: 10.1016/j.bspc.2024.106870 – ident: ref_38 doi: 10.3115/v1/D14-1179 – volume: 106 start-page: 249 year: 2018 ident: ref_59 article-title: A systematic study of the class imbalance problem in convolutional neural networks publication-title: Neural Netw. doi: 10.1016/j.neunet.2018.07.011 – ident: ref_41 doi: 10.3390/s17112556 – ident: ref_15 doi: 10.3390/machines13080669 – volume: 80 start-page: 241 year: 2022 ident: ref_57 article-title: Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges publication-title: Inf. Fusion doi: 10.1016/j.inffus.2021.11.006 – volume: 15 start-page: 1550147719853987 year: 2019 ident: ref_4 article-title: A concise review on sensor signal acquisition and transformation applied to human activity recognition and human–robot interaction publication-title: Int. J. Distrib. Sens. Netw. – volume: 16 start-page: 165 year: 2024 ident: ref_69 article-title: Human-Collaborative Artificial Intelligence Along With Social Values in Industry 5.0: A Survey of the State-of-the-Art publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/TCDS.2023.3326192 – ident: ref_52 doi: 10.3390/s25185765 – volume: 24 start-page: 568 year: 2016 ident: ref_7 article-title: Manual material handling: A classification scheme publication-title: Procedia Technol. doi: 10.1016/j.protcy.2016.05.114 – volume: 17 start-page: 160 year: 2017 ident: ref_45 article-title: An effective deep autoencoder approach for online smartphone-based human activity recognition publication-title: Int. J. Comput. Sci. Netw. Secur. – ident: ref_8 doi: 10.3390/s23042182 – volume: 51 start-page: 113 year: 2019 ident: ref_11 article-title: A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2019.02.011 – volume: 14 start-page: 939 year: 2023 ident: ref_25 article-title: A human activity recognition method using wearable sensors based on convtransformer model publication-title: Evol. Syst. doi: 10.1007/s12530-022-09480-y – volume: 49 start-page: 226 year: 2022 ident: ref_67 article-title: Challenges and opportunities in human robot collaboration context of Industry 4.0 - a state of the art review publication-title: Ind. Robot. Int. J. Robot. Res. Appl. doi: 10.1108/IR-04-2021-0077 – ident: ref_53 doi: 10.3390/s19040947 – volume: 11 start-page: 644 year: 2020 ident: ref_32 article-title: Using wearable sensors for human activity recognition in logistics: A comparison of different feature sets and machine learning algorithms publication-title: Int. J. Adv. Comput. Sci. Appl. – volume: 10 start-page: 4137 year: 2022 ident: ref_23 article-title: Convae-lstm: Convolutional autoencoder long short-term memory network for smartphone-based human activity recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3140373 – volume: 13 start-page: 139 year: 2020 ident: ref_2 article-title: Advanced sensing and human activity recognition in early intervention and rehabilitation of elderly people publication-title: J. Popul. Ageing doi: 10.1007/s12062-020-09260-z – volume: 8 start-page: 56855 year: 2020 ident: ref_51 article-title: LSTM-CNN architecture for human activity recognition publication-title: Access doi: 10.1109/ACCESS.2020.2982225 – ident: ref_54 doi: 10.1007/978-3-319-13817-6_11 – volume: 6 start-page: 107 year: 1998 ident: ref_37 article-title: The vanishing gradient problem during learning recurrent neural nets and problem solutions publication-title: Int. J. Uncertainty Fuzziness Knowl.-Based Syst. doi: 10.1142/S0218488598000094 – ident: ref_60 – ident: ref_66 doi: 10.3390/s21030777 – volume: 21 start-page: 24731 year: 2021 ident: ref_34 article-title: A Dataset of Human Motion and Muscular Activities in Manual Material Handling Tasks for Biomechanical and Ergonomic Analyses publication-title: Sens. J. doi: 10.1109/JSEN.2021.3113123 – ident: ref_49 doi: 10.3390/s22010134 – volume: 24 start-page: 292 year: 2019 ident: ref_63 article-title: TSE-CNN: A two-stage end-to-end CNN for human activity recognition publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2019.2909688 |
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| SubjectTerms | Adult Algorithms autoencoder Classification Comparative analysis convolutional neural network (CNN) Datasets Deep Learning Embedded systems Human Activities human activity recognition (HAR) Humans Information management manual material handling (MMH) Materials handling Neural networks Neural Networks, Computer recurrent neural network (RNN) Sensors Wearable Electronic Devices wearable sensor network (WSN) |
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