Soft fibers with magnetoelasticity for wearable electronics

Zhao, Xun; Zhou, Yihao; Xu, Jing; Chen, Guorui; Fang, Yunsheng; Tat, Trinny; Xiao, Xiao; Song, Yang; Li, Song; Chen, Jun

doi:10.1038/s41467-021-27066-1

Cited by 223 publications

(143 citation statements)

References 43 publications

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“…Very recently, the Chen Group at the University of California, Los Angeles discovered the giant magnetoelastic effect in both 2D soft systems [149,150] and 1D soft microfibers, [151] with up to 8.4 times enhancement of magnetomechanical coupling compared to that in the traditional bulky metal alloys. The magnetoelastic effect is the phenomenon that the magnetism of ferromagnetic material changes with the action of mechanical stress, as shown in Figure 3f.…”

Section: Magnetoelastic Effectmentioning

confidence: 99%

Wearable Pressure Sensors for Pulse Wave Monitoring

et al. 2022

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Furthermore, by 2030 this number is expected to rise steadily to 23.6 million. [2] Despite such high mortality rates, most CVD, [3,4] including arteriosclerosis, [5,6] diabetes, [7][8][9][10] myocardial infarction, [11,12] coronary heart disease, [13,14] and hypertension, [15][16][17][18] can be prevented and treated through early diagnosis and long-term monitoring of physiological signaling. Conventional health systems suffer from deficiencies in wearability, wireless technology, lifespan, and stability to maintain a long-term collection of clinical-grade individual health metrics for accurate diagnosis. [19][20][21] As such, promoting the utility of Internet of Things (IoT)-enabled technology in personalized healthcare is still significantly impeded by the need for costeffective and wearing-comfort biomedical devices to continuously provide real-time patient-generated health data. Over the past several decades, significant advances in wearable pressure sensors have been observed, allowing them to noninvasively and continuously detect human physiological and pathological signals. [22][23][24][25][26][27][28][29][30][31][32][33][34][35] These biomedical metrics can then be used to evaluate cardiovascular conditions, providing a personalized health care system with better health outcomes, increased user-friendliness, greater quality, and cost-effectiveness that are essential to reducing CVD incidence and mortality. [36][37][38][39] Pulse waves are prominent component of human physiological signaling and involve abundant human-health information that can reveal individual conditions, including heart problems (such as arrhythmia), blood pressure, vascular aging, exercise, medication, and sleep status. [40][41][42][43][44][45] Although physical symptoms are often elusively observed in their early stages, they can be diagnosed through subtle pulsewave changes. Preventive action of CVDs can be taken by differentiating the variance of pulse waveforms with consideration of participants' age, gender, weight, and daily diet. [46][47][48][49] Traditional Chinese medicine (TCM) has proposed empirical approaches to analyze human physical state from pulse waves, rendering pulse wave surveillance unavoidable for TCM. [50,51] TCM is unable to continuously monitor pulse waves, limiting the accuracy of the assessment results. As such, empirical diagnostic methods may profoundly depend on the experiences of the practitioner, the emotions of the participant, and the external environment, resulting in the administration of distorted or problematic treatment. [52] Additionally, the diagnostic results among practitioners are Cardiovascular diseases remain the leading cause of death worldwide. The rapid development of flexible sensing technologies and wearable pressure sensors have attracted keen research interest and have been widely used for longterm and real-time cardiovascular status monitoring. Owing to compelling characteristics, including light weight, wearing comfort, and high sensitivity to pulse pressures, physiological pulse ...

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Section: Magnetoelastic Effectmentioning

confidence: 99%

Wearable Pressure Sensors for Pulse Wave Monitoring

et al. 2022

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“…The magnetoelastic effect is the phenomenon in which the magnetism of some magnetic materials changes with mechanical deformation. , This effect is traditionally observed in some metals and metal alloys. , In 2021, the giant magnetoelastic effect was discovered in a soft system without the need of external magnetic fields by the Chen group at the University of California, Los Angeles, enabling the development of high-performance self-powered magnetoelastic sensors (Figure c). − The giant magnetoelastic effect observed in soft matter arises from changes in the micromagnet wavy chain structure under mechanical deformation. This effect differs from the traditional magnetoelastic effect seen in metal alloys, which is caused by stress-induced magnetic domain rearrangement under an externally applied magnetic field.…”

Section: Working Mechanismsmentioning

confidence: 99%

“…To resolve these issues, it is vitally important to exploit self-powered APT devices. − By leveraging biomechanical energy conversion, self-powered sensors are able to measure biomechanical motion by the generated electrical signals. − They can track and record the dynamic changes of biomarkers such as sound, − pulse wave, − and biomechanical motion ,,− using electrical signal profiles. Self-powered sensors based on piezoelectric, − triboelectric, ,− magnetoelastic − , and electromagnetic effect , have been developed in the community. Moreover, some self-powered sensors can be used in harsh and distributed environments to reduce the maintenance costs of replacing batteries .…”

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confidence: 99%

Machine-Learning-Aided Self-Powered Assistive Physical Therapy Devices

Xiao

Fang

et al. 2021

ACS Nano

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An expanding elderly population and people with disabilities pose considerable challenges to the current healthcare system. As a practical technology that integrates systems and services, assistive physical therapy devices are essential to maintain or to improve an individual’s functioning and independence, thus promoting their well-being. Given technological advancements, core components of self-powered sensors and optimized machine-learning algorithms will play innovative roles in providing assistive services for unmet global needs. In this Perspective, we provide an overview of the latest developments in machine-learning-aided assistive physical therapy devices based on emerging self-powered sensing systems and a discussion of the challenges and opportunities in this field.

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“…The rapid advancements of artificial intelligence (AI), the Internet of Things (IoT), and smart homes are renovating our lifestyles in meaningful and fundamental ways, and the cornerstone of this change is the robust and accurate communication between humans and machines. [1][2][3][4][5][6][7][8] The humanmachine interface, bridging the user and the machine, is a structured system of such communication that can involve speech or gesture, the latter of which is the widely adopted language for realization in the current community. [9][10][11][12][13][14][15] The ultimate goal of AI is to be comparable with the natural intelligence (e.g., consciousness and emotionality) displayed by humans, which is beyond the capability of gesture language for human-machine interaction.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Acoustic Interface for Wearable Human–Machine Interaction

Lin

Zhang

Xiao

et al. 2021

Adv Funct Materials

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Communication and interaction with machines are changing our ways of life. However, developing an acoustic interface that simultaneously features waterproofness, wearability, high fidelity, and high accuracy for human–machine interaction remains a grand challenge. Herein, a waterproof acoustic sensor (WAS) as a wearable translation interface to communicate with machines is reported. Owing to the sound‐response ability of internal microparticles, the WAS holds a significantly broad frequency response range of 0.1–20 kHz, covering almost the entire human audible range. The WAS is stable against human perspiration, shows omnidirectional response, and displays an excellent frequency detection resolution of 0.0001 kHz. With a collection of compelling features, the WAS can serve as a wearable acoustic human–machine interface and a high‐fidelity auditory platform for music recording. Moreover, the WAS‐based acoustic interface holds a remarkable 98% accuracy for speech recognition with the assistance of an artificial intelligence algorithm. Finally, the WAS‐based acoustic interface demonstrates speaker verification and identification for implementation in highly secure biometric authentication systems and wireless control of an intelligent car using speech recognition. Such a WAS‐based acoustic interface represents the advancement of high‐fidelity translation platforms for human–machine interactions toward practical applications, including the Internet of Things, assistive technology, and intelligent recognition systems.

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Soft fibers with magnetoelasticity for wearable electronics

Cited by 223 publications

References 43 publications

Wearable Pressure Sensors for Pulse Wave Monitoring

Wearable Pressure Sensors for Pulse Wave Monitoring

Machine-Learning-Aided Self-Powered Assistive Physical Therapy Devices

A Personalized Acoustic Interface for Wearable Human–Machine Interaction

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