Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors

Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	João M. Lopes [verfasserIn] Joana Figueiredo [verfasserIn] Pedro Fonseca [verfasserIn] João J. Cerqueira [verfasserIn] João P. Vilas-Boas [verfasserIn] Cristina P. Santos [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 20, p 7913
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:20, p 7913

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22207913

Katalog-ID:	DOAJ021642672

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allfields	10.3390/s22207913 doi (DE-627)DOAJ021642672 (DE-599)DOAJ8242a98108c84002a76310da858da395 DE-627 ger DE-627 rakwb eng TP1-1185 João M. Lopes verfasserin aut Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control. artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation Chemical technology Joana Figueiredo verfasserin aut Pedro Fonseca verfasserin aut João J. Cerqueira verfasserin aut João P. Vilas-Boas verfasserin aut Cristina P. Santos verfasserin aut In Sensors MDPI AG, 2003 22(2022), 20, p 7913 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:20, p 7913 https://doi.org/10.3390/s22207913 kostenfrei https://doaj.org/article/8242a98108c84002a76310da858da395 kostenfrei https://www.mdpi.com/1424-8220/22/20/7913 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 20, p 7913
spelling	10.3390/s22207913 doi (DE-627)DOAJ021642672 (DE-599)DOAJ8242a98108c84002a76310da858da395 DE-627 ger DE-627 rakwb eng TP1-1185 João M. Lopes verfasserin aut Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control. artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation Chemical technology Joana Figueiredo verfasserin aut Pedro Fonseca verfasserin aut João J. Cerqueira verfasserin aut João P. Vilas-Boas verfasserin aut Cristina P. Santos verfasserin aut In Sensors MDPI AG, 2003 22(2022), 20, p 7913 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:20, p 7913 https://doi.org/10.3390/s22207913 kostenfrei https://doaj.org/article/8242a98108c84002a76310da858da395 kostenfrei https://www.mdpi.com/1424-8220/22/20/7913 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 20, p 7913
allfields_unstemmed	10.3390/s22207913 doi (DE-627)DOAJ021642672 (DE-599)DOAJ8242a98108c84002a76310da858da395 DE-627 ger DE-627 rakwb eng TP1-1185 João M. Lopes verfasserin aut Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control. artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation Chemical technology Joana Figueiredo verfasserin aut Pedro Fonseca verfasserin aut João J. Cerqueira verfasserin aut João P. Vilas-Boas verfasserin aut Cristina P. Santos verfasserin aut In Sensors MDPI AG, 2003 22(2022), 20, p 7913 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:20, p 7913 https://doi.org/10.3390/s22207913 kostenfrei https://doaj.org/article/8242a98108c84002a76310da858da395 kostenfrei https://www.mdpi.com/1424-8220/22/20/7913 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 20, p 7913
allfieldsGer	10.3390/s22207913 doi (DE-627)DOAJ021642672 (DE-599)DOAJ8242a98108c84002a76310da858da395 DE-627 ger DE-627 rakwb eng TP1-1185 João M. Lopes verfasserin aut Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control. artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation Chemical technology Joana Figueiredo verfasserin aut Pedro Fonseca verfasserin aut João J. Cerqueira verfasserin aut João P. Vilas-Boas verfasserin aut Cristina P. Santos verfasserin aut In Sensors MDPI AG, 2003 22(2022), 20, p 7913 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:20, p 7913 https://doi.org/10.3390/s22207913 kostenfrei https://doaj.org/article/8242a98108c84002a76310da858da395 kostenfrei https://www.mdpi.com/1424-8220/22/20/7913 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 20, p 7913
allfieldsSound	10.3390/s22207913 doi (DE-627)DOAJ021642672 (DE-599)DOAJ8242a98108c84002a76310da858da395 DE-627 ger DE-627 rakwb eng TP1-1185 João M. Lopes verfasserin aut Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control. artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation Chemical technology Joana Figueiredo verfasserin aut Pedro Fonseca verfasserin aut João J. Cerqueira verfasserin aut João P. Vilas-Boas verfasserin aut Cristina P. Santos verfasserin aut In Sensors MDPI AG, 2003 22(2022), 20, p 7913 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:20, p 7913 https://doi.org/10.3390/s22207913 kostenfrei https://doaj.org/article/8242a98108c84002a76310da858da395 kostenfrei https://www.mdpi.com/1424-8220/22/20/7913 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 20, p 7913
language	English
source	In Sensors 22(2022), 20, p 7913 volume:22 year:2022 number:20, p 7913
sourceStr	In Sensors 22(2022), 20, p 7913 volume:22 year:2022 number:20, p 7913
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation Chemical technology
isfreeaccess_bool	true
container_title	Sensors
authorswithroles_txt_mv	João M. Lopes @@aut@@ Joana Figueiredo @@aut@@ Pedro Fonseca @@aut@@ João J. Cerqueira @@aut@@ João P. Vilas-Boas @@aut@@ Cristina P. Santos @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	331640910
id	DOAJ021642672
language_de	englisch
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callnumber-first	T - Technology
author	João M. Lopes
spellingShingle	João M. Lopes misc TP1-1185 misc artificial intelligence misc deep learning misc energy expenditure misc gait rehabilitation misc human-in-the-loop misc robotics-based rehabilitation misc Chemical technology Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
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topic_title	TP1-1185 Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors artificial intelligence deep learning energy expenditure gait rehabilitation human-in-the-loop robotics-based rehabilitation
topic	misc TP1-1185 misc artificial intelligence misc deep learning misc energy expenditure misc gait rehabilitation misc human-in-the-loop misc robotics-based rehabilitation misc Chemical technology
topic_unstemmed	misc TP1-1185 misc artificial intelligence misc deep learning misc energy expenditure misc gait rehabilitation misc human-in-the-loop misc robotics-based rehabilitation misc Chemical technology
topic_browse	misc TP1-1185 misc artificial intelligence misc deep learning misc energy expenditure misc gait rehabilitation misc human-in-the-loop misc robotics-based rehabilitation misc Chemical technology
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title	Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
ctrlnum	(DE-627)DOAJ021642672 (DE-599)DOAJ8242a98108c84002a76310da858da395
title_full	Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
author_sort	João M. Lopes
journal	Sensors
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author_browse	João M. Lopes Joana Figueiredo Pedro Fonseca João J. Cerqueira João P. Vilas-Boas Cristina P. Santos
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author-letter	João M. Lopes
doi_str_mv	10.3390/s22207913
author2-role	verfasserin
title_sort	deep learning-based energy expenditure estimation in assisted and non-assisted gait using inertial, emg, and heart rate wearable sensors
callnumber	TP1-1185
title_auth	Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
abstract	Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.
abstractGer	Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.
abstract_unstemmed	Energy expenditure is a key rehabilitation outcome and is starting to be used in robotics-based rehabilitation through human-in-the-loop control to tailor robot assistance towards reducing patients’ energy effort. However, it is usually assessed by indirect calorimetry which entails a certain degree of invasiveness and provides delayed data, which is not suitable for controlling robotic devices. This work proposes a deep learning-based tool for steady-state energy expenditure estimation based on more ergonomic sensors than indirect calorimetry. The study innovates by estimating the energy expenditure in assisted and non-assisted conditions and in slow gait speeds similarly to impaired subjects. This work explores and benchmarks the long short-term memory (LSTM) and convolutional neural network (CNN) as deep learning regressors. As inputs, we fused inertial data, electromyography, and heart rate signals measured by on-body sensors from eight healthy volunteers walking with and without assistance from an ankle-foot exoskeleton at 0.22, 0.33, and 0.44 m/s. LSTM and CNN were compared against indirect calorimetry using a leave-one-subject-out cross-validation technique. Results showed the suitability of this tool, especially CNN, that demonstrated root-mean-squared errors of 0.36 W/kg and high correlation (ρ < 0.85) between target and estimation (<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msup<<mrow<<mover accent="true"<<mrow<<mi mathvariant="normal"<R</mi<</mrow<<mo stretchy="true"<¯</mo<</mover<</mrow<<mn<2</mn<</msup<</mrow<</semantics<</math<</inline-formula< = 0.79). CNN was able to discriminate the energy expenditure between assisted and non-assisted gait, basal, and walking energy expenditure, throughout three slow gait speeds. CNN regressor driven by kinematic and physiological data was shown to be a more ergonomic technique for estimating the energy expenditure, contributing to the clinical assessment in slow and robotic-assisted gait and future research concerning human-in-the-loop control.
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title_short	Deep Learning-Based Energy Expenditure Estimation in Assisted and Non-Assisted Gait Using Inertial, EMG, and Heart Rate Wearable Sensors
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