49. Gao, W.; Zhang, L.; Huang, W.; Min, F.; He, J.; Song, A. Deep Neural Networks for sensor-based human activity recognition using selective kernel convolution. *IEEE Trans. Instrum. Meas.* **2021**, *70*, 2512313. [CrossRef]

50. Teng, Q.; Wang, K.; Zhang, L.; He, J. The layer-wise training Convolutional Neural Networks using local loss for sensor-based human activity recognition. *IEEE Sen. J.* **2020**, *20*, 7265-7274. [CrossRef]

51. Ronald, M.; Poulose, A.; Han, D.S. iSPLInception: An Inception-ResNet Deep Learning architecture for human activity recognition. *IEEE Access* **2021**, *9*, 68985-69001. [CrossRef]

52. Challa, S.K.; Kumar, A.; Semwal, V.B. A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data. *Vis. Comput.* **2022**, *38*, 4095-4109. [CrossRef]

53. Xia, K.; Huang, J.; Wang, H. LSTM-CNN Architecture for human activity recognition. *IEEE Access* **2020**, *8*, 56855-56866. [CrossRef]

54. Ignatov, A. Real-time human activity recognition from accelerometer data using Convolutional Neural Networks. *Appl. Soft Comput.* **2018**, *62*, 915-922. [CrossRef]

55. Mekruksavanich, S.; Jitpattanakul, A. LSTM networks using smartphone data for sensor-based human activity recognition in smart homes. *Sensors* **2021**, *21*, 1636. [CrossRef]

56. Morshedlou, F.; Ravanshad, N.; Rezaee-Dehsorkh, H. An ultra-low power analog QRS-detection circuit for ambulatory ECG monitoring. *AEU-Int. J. Electron. Commun.* **2021**, *129*, 153551. [CrossRef]

57. Gündör, C.B.; Töreyin, H. A 0.5 nW analog ECG processor for real time R-wave detection based on Pan-Tompkins algorithm. In Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA, 19-22 May 2019; pp. 1-4. [CrossRef]

58. Gündör, C.B.; Mercier, P.P.; Töreyin, H. A 1.2nW analog electrocardiogram processor achieving a 99.63% QRS complex detection sensitivity. *IEEE Trans. Biomed. Circuits Syst.* **2021**, *15*, 617-628. [CrossRef]

59. Liu, H.; Xue, T.; Schultz, T. On a real real-time wearable human activity recognition system. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies-Volume 5: WHC, INSTICC, SciTePress, Lisbon, Portugal, 16-18 February 2023; pp. 711-720. [CrossRef]

60. Liu, H.; Schultz, T. How long are various types of daily activities? Statistical analysis of a multimodal wearable sensor-based human activity dataset. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022)-Volume 5: HEALTHINF. INSTICC, Virtual Event, 9-11 February 2022; SciTePress: Setubal, Portugal, 2022; pp. 680-688. [CrossRef]

61. Liu, H.; Hartmann, Y.; Schultz, T. CSL-SHARE: A multimodal wearable sensor-based human activity dataset. *Front. Comput. Sci.* **2021**, *3*, 759136. [CrossRef]

62. Bhamra, H.; Lynch, J.; Ward, M.; Irazoqui, P. A noise-power-area optimized biosensing front end for Wireless Body Sensor Nodes and medical implantable devices. *IEEE Trans. Very Large Scale Integr. (VLSI) Syst.* **2017**, *25*, 2917-2928. [CrossRef]

63. Vafaei, M.; Parhizgar, A.; Abiri, E.; Salehi, M.R. A low power and ultra-high input impedance analog front end based on fully differential difference inverter-based amplifier for biomedical applications. *AEU-Int. J. Electron. Commun.* **2021**, *142*, 154005. [CrossRef]

64. Zeng, Y.; Li, Z.; Chen, W.; Zhou, W.; Bao, Y.; Chen, Y.; Li, Y. A 672 nW, 670 nVrms ECG acquisition AFE with noise tolerant heartbeat detector. *IEEE Open J. Circuits Syst.* **2023**, *4*, 25-35. [CrossRef]

65. Tang, F.; Wu, N.; Yang, T.; Wang, H. A-110 dB THD rail-to-rail class-AB programmable gain amplifier with common-mode-detection-based transconductance linearization scheme. *Microelectron. J.* **2022**, *128*, 105511. [CrossRef]

66. Hsu, Y.P.; Liu, Z.; Hella, M.M. A 12.3-µW 0.72-mm² fully integrated front-end IC for arterial pulse waveform and ExG recording. *IEEE J. Solid-State Circuits* **2020**, *55*, 2756-2770. [CrossRef]

67. Zhang, T.; Li, Y.; Su, C.; Zhang, X.; Yang, Y. A 1V 3.5 µW Bio-AFE with chopper-capacitor-chopper integrator-based DSL and low power Gm-C filter. *IEEE Trans. Circuits Syst. II Express Briefs* **2022**, *69*, 5-9. [CrossRef]

68. Pérez-Bailón, J.; Calvo, B.; Medrano, N.; Ramírez-Angulo, J. A 1.8 V Gm-C highly tunable low pass filter for sensing applications. In Proceedings of the 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia, 25-28 May 2020; pp. 1-5. [CrossRef]

69. Pérez-Bailón, J.; Márquez, A.; Calvo, B.; Medrano, N. A 0.18µm CMOS widely tunable Low-Pass Filter with sub-hz cutoff frequencies. In Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 27-30 May 2018; pp. 1-4. [CrossRef]

70. Rodriguez-Villegas, E.; Casson, A.J.; Corbishley, P. A subhertz nanopower Low-Pass Filter. *IEEE Trans. Circuits Syst. II Express Briefs* **2011**, *58*, 351-355. [CrossRef]

71. Kuo, F.W.; Binsfeld Ferreira, S.; Chen, H.N.R.; Cho, L.C.; Jou, C.P.; Hsueh, F.L.; Madadi, I.; Tohidian, M.; Shahmohammadi, M.; Babaie, M.; et al. A Bluetooth Low-Energy transceiver with 3.7-mW all-digital transmitter, 2.75-mW high-IF discrete-time receiver, and TX/RX switchable on-chip matching network. *IEEE J. Solid-State Circuits* **2017**, *52*, 1144-1162. [CrossRef]

72. Gaikwad, N.B.; Tiwari, V.; Keskar, A.; Shivaprakash, N.C. Efficient FPGA implementation of multilayer perceptron for real-time human activity classification. *IEEE Access* **2019**, *7*, 26696-26706. [CrossRef]

73. Hu, Q.; Tang, X.; Tang, W. A smart chair sitting posture recognition system using flex sensors and FPGA implemented Artificial Neural Network. *IEEE Sen. J.* **2020**, *20*, 8007-8016. [CrossRef]

74. James Romaine, B.; Martín, M.P. High-throughput low power area efficient 17-bit 2's complement multilayer perceptron components and architecture for on-chip Machine Learning in implantable devices. *IEEE Access* **2022**, *10*, 92516-92531. [CrossRef]
49. Gao, W.; Zhang, L.; Huang, W.; Min, F.; He, J.; Song, A. Deep Neural Networks for sensor-based human activity recognition using selective kernel convolution. *IEEE Trans. Instrum. Meas.* **2021**, *70*, 2512313. [CrossRef]

50. Teng, Q.; Wang, K.; Zhang, L.; He, J. The layer-wise training Convolutional Neural Networks using local loss for sensor-based human activity recognition. *IEEE Sen. J.* **2020**, *20*, 7265-7274. [CrossRef]

51. Ronald, M.; Poulose, A.; Han, D.S. iSPLInception: An Inception-ResNet Deep Learning architecture for human activity recognition. *IEEE Access* **2021**, *9*, 68985-69001. [CrossRef]

52. Challa, S.K.; Kumar, A.; Semwal, V.B. A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data. *Vis. Comput.* **2022**, *38*, 4095-4109. [CrossRef]

53. Xia, K.; Huang, J.; Wang, H. LSTM-CNN Architecture for human activity recognition. *IEEE Access* **2020**, *8*, 56855-56866. [CrossRef]

54. Ignatov, A. Real-time human activity recognition from accelerometer data using Convolutional Neural Networks. *Appl. Soft Comput.* **2018**, *62*, 915-922. [CrossRef]

55. Mekruksavanich, S.; Jitpattanakul, A. LSTM networks using smartphone data for sensor-based human activity recognition in smart homes. *Sensors* **2021**, *21*, 1636. [CrossRef]

56. Morshedlou, F.; Ravanshad, N.; Rezaee-Dehsorkh, H. An ultra-low power analog QRS-detection circuit for ambulatory ECG monitoring. *AEU-Int. J. Electron. Commun.* **2021**, *129*, 153551. [CrossRef]

57. Güngör, C.B.; Töreyin, H. A 0.5 nW analog ECG processor for real time R-wave detection based on Pan-Tompkins algorithm. In Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA, 19-22 May 2019; pp. 1-4. [CrossRef]

58. Güngör, C.B.; Mercier, P.P.; Töreyin, H. A 1.2nW analog electrocardiogram processor achieving a 99.63% QRS complex detection sensitivity. *IEEE Trans. Biomed. Circuits Syst.* **2021**, *15*, 617-628. [CrossRef]

59. Liu, H.; Xue, T.; Schultz, T. On a real real-time wearable human activity recognition system. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies-Volume 5: WHC, INSTICC, SciTePress, Lisbon, Portugal, 16-18 February 2023; pp. 711-720. [CrossRef]

60. Liu, H.; Schultz, T. How long are various types of daily activities? Statistical analysis of a multimodal wearable sensor-based human activity dataset. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022)-Volume 5: HEALTHINF. INSTICC, Virtual Event, 9-11 February 2022; SciTePress: Setubal, Portugal, 2022; pp. 680-688. [CrossRef]

61. Liu, H.; Hartmann, Y.; Schultz, T. CSL-SHARE: A multimodal wearable sensor-based human activity dataset. *Front. Comput. Sci.* **2021**, *3*, 759136. [CrossRef]

62. Bhamra, H.; Lynch, J.; Ward, M.; Irazoqui, P. A noise-power-area optimized biosensing front end for Wireless Body Sensor Nodes and medical implantable devices. *IEEE Trans. Very Large Scale Integr. (VLSI) Syst.* **2017**, *25*, 2917-2928. [CrossRef]

63. Vafaei, M.; Parhizgar, A.; Abiri, E.; Salehi, M.R. A low power and ultra-high input impedance analog front end based on fully differential difference inverter-based amplifier for biomedical applications. *AEU-Int. J. Electron. Commun.* **2021**, *142*, 154005. [CrossRef]

64. Zeng, Y.; Li, Z.; Chen, W.; Zhou, W.; Bao, Y.; Chen, Y.; Li, Y. A 672 nW, 670 nVrms ECG acquisition AFE with noise tolerant heartbeat detector. *IEEE Open J. Circuits Syst.* **2023**, *4*, 25-35. [CrossRef]

65. Tang, F.; Wu, N.; Yang, T.; Wang, H. A-110 dB THD rail-to-rail class-AB programmable gain amplifier with common-mode-detection-based transconductance linearization scheme. *Microelectron. J.* **2022**, *128*, 105511. [CrossRef]

66. Hsu, Y.P.; Liu, Z.; Hella, M.M. A 12.3-µW 0.72-mm² fully integrated front-end IC for arterial pulse waveform and ExG recording. *IEEE J. Solid-State Circuits* **2020**, *55*, 2756-2770. [CrossRef]

67. Zhang, T.; Li, Y.; Su, C.; Zhang, X.; Yang, Y. A 1V 3.5 µW Bio-AFE with chopper-capacitor-chopper integrator-based DSL and low power Gm-C filter. *IEEE Trans. Circuits Syst. II Express Briefs* **2022**, *69*, 5-9. [CrossRef]

68. Pérez-Bailón, J.; Calvo, B.; Medrano, N.; Ramírez-Angulo, J. A 1.8 V Gm-C highly tunable low pass filter for sensing applications. In Proceedings of the 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia, 25-28 May 2020; pp. 1-5. [CrossRef]

69. Pérez-Bailón, J.; Márquez, A.; Calvo, B.; Medrano, N. A 0.18µm CMOS widely tunable Low-Pass Filter with sub-hz cutoff frequencies. In Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, Italy, 27-30 May 2018; pp. 1-4. [CrossRef]

70. Rodriguez-Villegas, E.; Casson, A.J.; Corbishley, P. A subhertz nanopower Low-Pass Filter. *IEEE Trans. Circuits Syst. II Express Briefs* **2011**, *58*, 351-355. [CrossRef]

71. Kuo, F.W.; Binsfeld Ferreira, S.; Chen, H.N.R.; Cho, L.C.; Jou, C.P.; Hsueh, F.L.; Madadi, I.; Tohidian, M.; Shahmohammadi, M.; Babaie, M.; et al. A Bluetooth Low-Energy transceiver with 3.7-mW all-digital transmitter, 2.75-mW high-IF discrete-time receiver, and TX/RX switchable on-chip matching network. *IEEE J. Solid-State Circuits* **2017**, *52*, 1144-1162. [CrossRef]

72. Gaikwad, N.B.; Tiwari, V.; Keskar, A.; Shivaprakash, N.C. Efficient FPGA implementation of multilayer perceptron for real-time human activity classification. *IEEE Access* **2019**, *7*, 26696-26706. [CrossRef]

73. Hu, Q.; Tang, X.; Tang, W. A smart chair sitting posture recognition system using flex sensors and FPGA implemented Artificial Neural Network. *IEEE Sen. J.* **2020**, *20*, 8007-8016. [CrossRef]

74. James Romaine, B.; Martín, M.P. High-throughput low power area efficient 17-bit 2's complement multilayer perceptron components and architecture for on-chip Machine Learning in implantable devices. *IEEE Access* **2022**, *10*, 92516-92531. [CrossRef]
75. Moons, B.; Bankman, D.; Yang, L.; Murmann, B.; Verhelst, M. BinarEye: An always-on energy-accuracy-scalable binary CNN processor with all memory on chip in 28 nm CMOS. In Proceedings of the 2018 IEEE Custom Integrated Circuits Conference (CICC), San Diego, CA, USA, 8–11 April 2018; pp. 1–4. [CrossRef]

76. Wang, X.; Magno, M.; Cavigelli, L.; Benini, L. FANN-on-MCU: An open-source toolkit for energy-efficient Neural Network inference at the edge of the Internet of Things. *IEEE Internet Things J.* **2020**, *7*, 4403–4417. [CrossRef]

77. Bahr, A.; Schneider, M.; Francis, M.A.; Lehmann, H.M.; Barg, I.; Buschhoff, A.S.; Wulff, P.; Strunskus, T.; Faupel, F. Epileptic seizure detection on an ultra-low-power embedded RISC-V processor using a Convolutional Neural Network. *Biosensors* **2021**, *11*, 203. [CrossRef]

78. Yin, Y.; Abubakar, S.M.; Tan, S.; Jiang, H.; Wang, Z.; U, S.P.; Jia, W. A 17.7-pJ/Cycle ECG processor for arrhythmia detection with high immunity to power line interference and baseline drift. In Proceedings of the 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC), Hiroshima, Japan, 9–11 November 2020; pp. 1–4. [CrossRef]

79. Yin, Y.; Abubakar, S.M.; Tan, S.; Shi, J.; Yang, P.; Yang, W.; Jiang, H.; Wang, Z.; Jia, W.; U, S-P. A 2.63 µW ECG processor with adaptive arrhythmia detection and data compression for implantable cardiac monitoring device. *IEEE Trans. Biomed. Circuits Syst.* **2021**, *15*, 777–790. [CrossRef]

80. Deng, J.; Ma, J.; Yang, J.; Liu, S.; Chen, H.; Wang, X.; Zhang, X. An energy-efficient ECG processor based on HDWT and a hybrid classifier for arrhythmia detection. *Appl. Sci.* **2024**, *14*, 342. [CrossRef]

81. Basterretxea, K.; Echanobe, J.; del Campo, I. A wearable human activity recognition system on a chip. In Proceedings of the 2014 Conference on Design and Architectures for Signal and Image Processing, Madrid, Spain, 8–10 October 2014; pp. 1–8. [CrossRef]

82. Bhat, G.; Deb, R.; Chaurasia, V.V.; Shill, H.; Ogras, U.Y. Online human activity recognition using low-power wearable devices. In Proceedings of the 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, CA, USA, 5–8 November 2018; pp. 1–8. [CrossRef]

83. Bhat, G.; Tuncel, Y.; An, S.; Lee, H.G.; Ogras, U.Y. An ultra-low energy human activity recognition accelerator for wearable health applications. *ACM Trans. Embed. Comput. Syst.* **2019**, *18*, 49:1–49:22. [CrossRef]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
75. Moons, B.; Bankman, D.; Yang, L.; Murmann, B.; Verhelst, M. BinarEye: An always-on energy-accuracy-scalable binary CNN processor with all memory on chip in 28 nm CMOS. In Proceedings of the 2018 IEEE Custom Integrated Circuits Conference (CICC), San Diego, CA, USA, 8–11 April 2018; pp. 1–4. [CrossRef]

76. Wang, X.; Magno, M.; Cavigelli, L.; Benini, L. FANN-on-MCU: An open-source toolkit for energy-efficient Neural Network inference at the edge of the Internet of Things. *IEEE Internet Things J.* **2020**, *7*, 4403–4417. [CrossRef]

77. Bahr, A.; Schneider, M.; Francis, M.A.; Lehmann, H.M.; Barg, I.; Buschhoff, A.S.; Wulff, P.; Strunskus, T.; Faupel, F. Epileptic seizure detection on an ultra-low-power embedded RISC-V processor using a Convolutional Neural Network. *Biosensors* **2021**, *11*, 203. [CrossRef]

78. Yin, Y.; Abubakar, S.M.; Tan, S.; Jiang, H.; Wang, Z.; U, S.P.; Jia, W. A 17.7-pJ/Cycle ECG processor for arrhythmia detection with high immunity to power line interference and baseline drift. In Proceedings of the 2020 IEEE Asian Solid-State Circuits Conference (A-SSCC), Hiroshima, Japan, 9–11 November 2020; pp. 1–4. [CrossRef]

79. Yin, Y.; Abubakar, S.M.; Tan, S.; Shi, J.; Yang, P.; Yang, W.; Jiang, H.; Wang, Z.; Jia, W.; U, S-P. A 2.63 µW ECG processor with adaptive arrhythmia detection and data compression for implantable cardiac monitoring device. *IEEE Trans. Biomed. Circuits Syst.* **2021**, *15*, 777–790. [CrossRef]

80. Deng, J.; Ma, J.; Yang, J.; Liu, S.; Chen, H.; Wang, X.; Zhang, X. An energy-efficient ECG processor based on HDWT and a hybrid classifier for arrhythmia detection. *Appl. Sci.* **2024**, *14*, 342. [CrossRef]

81. Basterretxea, K.; Echanobe, J.; del Campo, I. A wearable human activity recognition system on a chip. In Proceedings of the 2014 Conference on Design and Architectures for Signal and Image Processing, Madrid, Spain, 8–10 October 2014; pp. 1–8. [CrossRef]

82. Bhat, G.; Deb, R.; Chaurasia, V.V.; Shill, H.; Ogras, U.Y. Online human activity recognition using low-power wearable devices. In Proceedings of the 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, CA, USA, 5–8 November 2018; pp. 1–8. [CrossRef]

83. Bhat, G.; Tuncel, Y.; An, S.; Lee, H.G.; Ogras, U.Y. An ultra-low energy human activity recognition accelerator for wearable health applications. *ACM Trans. Embed. Comput. Syst.* **2019**, *18*, 49:1–49:22. [CrossRef]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.