With increasing work pressure in modern society,prolonged sedentary positions with poor sitting postures can cause physical and psychological problems,including obesity,muscular disorders,and myopia.In this paper,we p...With increasing work pressure in modern society,prolonged sedentary positions with poor sitting postures can cause physical and psychological problems,including obesity,muscular disorders,and myopia.In this paper,we present a self-powered sitting position monitoring vest(SPMV)based on triboelectric nanogenerators(TENGs)to achieve accurate real-time posture recognition through an integrated machine learning algorithm.The SPMV achieves high sensitivity(0.16 mV/Pa),favorable stretchability(10%),good stability(12,000 cycles),and machine washability(10 h)by employing knitted double threads interlaced with conductive fiber and nylon yarn.Utilizing a knitted structure and sensor arrays that are stitched into different parts of the clothing,the SPMV offers a non-invasive method of recognizing different sitting postures,providing feedback,and warning users while enhancing long-term wearing comfortability.It achieves a posture recognition accuracy of 96.6%using the random forest classifier,which is higher than the logistic regression(95.5%)and decision tree(94.3%)classifiers.The TENG-based SPMV offers a reliable solution in the healthcare system for non-invasive and long-term monitoring,promoting the development of triboelectric-based wearable electronics.展开更多
Tactile sensors are essential components of wearable electronic devices,but there are still various problems in terms of energy supply,flexibility and skin adaptability.In this paper,we report a self-powered flexible ...Tactile sensors are essential components of wearable electronic devices,but there are still various problems in terms of energy supply,flexibility and skin adaptability.In this paper,we report a self-powered flexible tactile sensor(FTS)mainly composed of a BaTiO_(3)/polyacrylonitrile/Ecoflex(BTO/PAN/Ecoflex)composite film,which can be used for dynamically monitoring human plantar pressure,posture and other physiological and motion parameters.Combining the synergistic piezoelectric properties of PAN and BTO,the output voltage/current of the BTO/PAN/Ecoflex composite film is 4.5/5.8 times that of the BTO/Ecoflex composite film,with maximum instantaneous power that can reach up to 3.375μW.Under the action of external pressure stress,the FTS can reach a normalized voltage sensitivity and voltage linearity of 0.54 V/N and 0.98,respectively.Furthermore,a human-machine interaction test system is built,which can display the stress changes of human body monitoring parts in real time according to voltage changes and different color assignments.The developed human-machine interaction test system provides a new idea for the diagnosis of flatfoot and other medical diseases.Hence,this work proposes new FTSs that use a BTO/PAN/Ecoflex composite film with high sensitivity and great output performance,thus exhibiting immense potential application prospects in medical research,personalized recognition and human-machine interaction.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
基金the National Key R&D Program of China(No.2021YFA1201601)the National Natural Science Foundation of China(No.22109012)+1 种基金Natural Science Foundation of Beijing(No.2212052)the Fundamental Research Funds for the Central Universities(No.E1E46805).
文摘With increasing work pressure in modern society,prolonged sedentary positions with poor sitting postures can cause physical and psychological problems,including obesity,muscular disorders,and myopia.In this paper,we present a self-powered sitting position monitoring vest(SPMV)based on triboelectric nanogenerators(TENGs)to achieve accurate real-time posture recognition through an integrated machine learning algorithm.The SPMV achieves high sensitivity(0.16 mV/Pa),favorable stretchability(10%),good stability(12,000 cycles),and machine washability(10 h)by employing knitted double threads interlaced with conductive fiber and nylon yarn.Utilizing a knitted structure and sensor arrays that are stitched into different parts of the clothing,the SPMV offers a non-invasive method of recognizing different sitting postures,providing feedback,and warning users while enhancing long-term wearing comfortability.It achieves a posture recognition accuracy of 96.6%using the random forest classifier,which is higher than the logistic regression(95.5%)and decision tree(94.3%)classifiers.The TENG-based SPMV offers a reliable solution in the healthcare system for non-invasive and long-term monitoring,promoting the development of triboelectric-based wearable electronics.
基金supported by the National Key R&D Program of China(Grant Nos. 2019YFF0301802, 2019YFB2004802 and 2018YFF0300605)the National Natural Science Foundation of China (Grant Nos. 62101513,52175554, 51975542)+1 种基金the Applied Fundamental Research Program of Shanxi Province (Grant Nos. 201901D111146, 20210302124170)Shanxi “1331 Project” Key Subject Construction (Grant No. 1331KSC)
文摘Tactile sensors are essential components of wearable electronic devices,but there are still various problems in terms of energy supply,flexibility and skin adaptability.In this paper,we report a self-powered flexible tactile sensor(FTS)mainly composed of a BaTiO_(3)/polyacrylonitrile/Ecoflex(BTO/PAN/Ecoflex)composite film,which can be used for dynamically monitoring human plantar pressure,posture and other physiological and motion parameters.Combining the synergistic piezoelectric properties of PAN and BTO,the output voltage/current of the BTO/PAN/Ecoflex composite film is 4.5/5.8 times that of the BTO/Ecoflex composite film,with maximum instantaneous power that can reach up to 3.375μW.Under the action of external pressure stress,the FTS can reach a normalized voltage sensitivity and voltage linearity of 0.54 V/N and 0.98,respectively.Furthermore,a human-machine interaction test system is built,which can display the stress changes of human body monitoring parts in real time according to voltage changes and different color assignments.The developed human-machine interaction test system provides a new idea for the diagnosis of flatfoot and other medical diseases.Hence,this work proposes new FTSs that use a BTO/PAN/Ecoflex composite film with high sensitivity and great output performance,thus exhibiting immense potential application prospects in medical research,personalized recognition and human-machine interaction.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
