Meditation accuracy detection system using deep learning

Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learnin...
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Autor*in:	Welekar, Rashmi [verfasserIn] Dubey, Anuradha Hablani, Sheetal

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Meditation accuracy Pose detection Blink detection

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 82(2023), 28 vom: 19. Apr., Seite 43625-43633
Übergeordnetes Werk:	volume:82 ; year:2023 ; number:28 ; day:19 ; month:04 ; pages:43625-43633

Links:	Volltext

DOI / URN:	10.1007/s11042-023-15273-5

Katalog-ID:	SPR053562739

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allfields	10.1007/s11042-023-15273-5 doi (DE-627)SPR053562739 (SPR)s11042-023-15273-5-e DE-627 ger DE-627 rakwb eng Welekar, Rashmi verfasserin aut Meditation accuracy detection system using deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. Meditation accuracy (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Blink detection (dpeaa)DE-He213 Dubey, Anuradha aut Hablani, Sheetal aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 19. Apr., Seite 43625-43633 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:19 month:04 pages:43625-43633 https://dx.doi.org/10.1007/s11042-023-15273-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 19 04 43625-43633
spelling	10.1007/s11042-023-15273-5 doi (DE-627)SPR053562739 (SPR)s11042-023-15273-5-e DE-627 ger DE-627 rakwb eng Welekar, Rashmi verfasserin aut Meditation accuracy detection system using deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. Meditation accuracy (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Blink detection (dpeaa)DE-He213 Dubey, Anuradha aut Hablani, Sheetal aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 19. Apr., Seite 43625-43633 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:19 month:04 pages:43625-43633 https://dx.doi.org/10.1007/s11042-023-15273-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 19 04 43625-43633
allfields_unstemmed	10.1007/s11042-023-15273-5 doi (DE-627)SPR053562739 (SPR)s11042-023-15273-5-e DE-627 ger DE-627 rakwb eng Welekar, Rashmi verfasserin aut Meditation accuracy detection system using deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. Meditation accuracy (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Blink detection (dpeaa)DE-He213 Dubey, Anuradha aut Hablani, Sheetal aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 19. Apr., Seite 43625-43633 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:19 month:04 pages:43625-43633 https://dx.doi.org/10.1007/s11042-023-15273-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 19 04 43625-43633
allfieldsGer	10.1007/s11042-023-15273-5 doi (DE-627)SPR053562739 (SPR)s11042-023-15273-5-e DE-627 ger DE-627 rakwb eng Welekar, Rashmi verfasserin aut Meditation accuracy detection system using deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. Meditation accuracy (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Blink detection (dpeaa)DE-He213 Dubey, Anuradha aut Hablani, Sheetal aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 19. Apr., Seite 43625-43633 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:19 month:04 pages:43625-43633 https://dx.doi.org/10.1007/s11042-023-15273-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 19 04 43625-43633
allfieldsSound	10.1007/s11042-023-15273-5 doi (DE-627)SPR053562739 (SPR)s11042-023-15273-5-e DE-627 ger DE-627 rakwb eng Welekar, Rashmi verfasserin aut Meditation accuracy detection system using deep learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. Meditation accuracy (dpeaa)DE-He213 Pose detection (dpeaa)DE-He213 Blink detection (dpeaa)DE-He213 Dubey, Anuradha aut Hablani, Sheetal aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 19. Apr., Seite 43625-43633 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:19 month:04 pages:43625-43633 https://dx.doi.org/10.1007/s11042-023-15273-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 19 04 43625-43633
language	English
source	Enthalten in Multimedia tools and applications 82(2023), 28 vom: 19. Apr., Seite 43625-43633 volume:82 year:2023 number:28 day:19 month:04 pages:43625-43633
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topic_facet	Meditation accuracy Pose detection Blink detection
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author	Welekar, Rashmi
spellingShingle	Welekar, Rashmi misc Meditation accuracy misc Pose detection misc Blink detection Meditation accuracy detection system using deep learning
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title_sort	meditation accuracy detection system using deep learning
title_auth	Meditation accuracy detection system using deep learning
abstract	Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Meditation is an effective technique for reducing stress, enhancing mental health, and enhancing overall wellbeing. However, the efficacy of meditation can be diminished if practitioners do not achieve the necessary level of concentration and precision. To address this issue, a Deep Learning-based Meditation Accuracy Detection System is proposed. Using EEG (electroencephalogram) signals, the system detects the precision of meditation. Using a large dataset of EEG signals collected from experienced meditators, a deep learning model is trained. The model is able to recognize patterns and characteristics in the EEG signals that indicate the level of concentration and precision attained during meditation. Potential applications of the proposed system include enhancing the efficacy of meditation practices, assisting individuals in monitoring their progress, and enabling researchers to investigate the neural mechanisms underlying meditations. Preliminary findings indicate that the proposed system can detect the level of meditation precision with high precision. This system has the potential to revolutionize the field of meditation by providing practitioners with objective feedback, facilitating the creation of personalized meditation programme, and allowing researchers to study the neural mechanisms underlying meditations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	28
title_short	Meditation accuracy detection system using deep learning
url	https://dx.doi.org/10.1007/s11042-023-15273-5
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author2	Dubey, Anuradha Hablani, Sheetal
author2Str	Dubey, Anuradha Hablani, Sheetal
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doi_str	10.1007/s11042-023-15273-5
up_date	2024-07-03T20:25:28.434Z
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