Smart Dream-Journal Brainwave Recorder Market Outlook

According to our latest research, the global smart dream-journal brainwave recorder market size reached USD 1.02 billion in 2024, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 15.4% from 2025 to 2033, reaching a forecasted value of USD 3.97 billion by 2033. This dynamic growth is primarily driven by increasing consumer interest in sleep health, rapid advancements in neurotechnology, and the integration of artificial intelligence (AI) for more precise and personalized dream analysis. As per the latest research, the surge in demand for non-invasive brainwave monitoring solutions, coupled with the growing popularity of wearable health devices, continues to propel the smart dream-journal brainwave recorder market globally.

One of the most significant growth factors for the smart dream-journal brainwave recorder market is the rising global awareness of sleep disorders and their impact on overall health. With mounting evidence linking quality sleep to cognitive performance, emotional wellbeing, and chronic disease prevention, both consumers and healthcare professionals are increasingly turning to advanced technologies for sleep monitoring and improvement. The proliferation of sleep-related studies and the growing prevalence of conditions such as insomnia, sleep apnea, and REM behavior disorder have underscored the need for reliable, user-friendly solutions. As a result, smart dream-journal brainwave recorders have gained traction not only as tools for personal wellness but also as integral components in clinical and research settings, supporting both self-monitoring and professional diagnosis.

Technological advancements form another critical pillar underpinning market growth. The integration of sophisticated sensors, electroencephalogram (EEG) technologies, and AI-driven analytics enables these devices to capture, interpret, and store detailed brainwave and dream data with unprecedented accuracy. This technological evolution has led to the development of compact, comfortable, and highly functional wearable and non-wearable devices capable of continuous monitoring and real-time feedback. Additionally, the synergy between smart devices and mobile applications has empowered users to access personalized insights, track sleep and dream patterns over time, and share data with healthcare providers seamlessly. The ongoing miniaturization of hardware and improvements in battery life further enhance user experience, making these devices more accessible and appealing to a broader demographic.

The market is also experiencing a surge in demand due to the growing trend of self-quantification and the broader movement toward personalized healthcare. Consumers are increasingly motivated to take proactive control of their health, leveraging data-driven tools to optimize sleep quality and mental wellness. This trend is particularly pronounced among younger, tech-savvy populations who value convenience, customization, and connectivity. The smart dream-journal brainwave recorder market has responded by offering a diverse range of products tailored to individual preferences, from sleek, app-integrated wearables to sophisticated, research-grade monitoring systems. As more individuals seek to understand the subconscious processes underlying their dreams and sleep cycles, the market is poised for sustained expansion, fueled by a blend of consumer curiosity, clinical necessity, and technological innovation.

From a regional perspective, North America currently leads the smart dream-journal brainwave recorder market, driven by high healthcare expenditure, widespread adoption of digital health technologies, and a strong culture of wellness innovation. Europe follows closely, supported by robust research infrastructure and growing public awareness of sleep health. The Asia Pacific region is emerging as a high-growth market, propelled by rising disposable incomes, increasing urbanization, and a burgeoning middle class with a keen interest in health and wellness. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with improving healthcare infrastructure and growing investments in digital health technologies. Regional variations in regulatory frameworks, consumer preferences, and technological penetration will continue to shape the competitive landscape and growth trajectory of the smart dream-journal brainwave recorder market over the forecast period.

A comparison of some state-of-the-art Digital Twin research papers in healthcare.

We are working to develop a comprehensive dataset of surgical tools based on specialities, with a hierarchical structure ��� speciality, pack, set and tool. We belive that this dataset can be useful for computer vision and deep learning research into surgical tool tracking, management and surgical training and audit. We have therefore created an initial dataset of surgical tool (instrument and implant) images, captured using under different lighting conditions and with different backgrounds. We captured RGB images of surgical tools using a DSLR camera and webcam on site in a major hospital under realistic conditions and with the surgical tools currently in use. Image backgrounds in our initial dataset were essentially flat colours, even though different colour backgrounds were used. As we further developed our dataset, we will try to include much greater occlusions, illumination changes, and the presence of blood, tissue and smoke in the images which would be more reflective of crowded, messy, real-world conditions. Illumination sources included natural light ��� direct sunlight and shaded light ��� LED, halogen and fluorescent lighting, and this accurately reflected the illumination working conditions within the hospital. Distances of the surgical tools to the camera to the object ranged from 60 to 150 cms., and the average class size was 74 images. Images captured included individual object images as well as cluttered, clustered and occluded objects. Our initial focus was on Orthopaedics and General Surgery, two out of the 14 surgical specialities. We selected these specialities since general surgery instruments are the most commonly used tools across all surgeries and provide instrument volume, while orthopaedics provides variety and complexity given the wide range of procedures, instruments and implants used in orthopaedic surgery. We will add other specialities as we develop this dataset, to reflect the complexities inherent in each of the surgical specialities. This dataset was designed to offer a large variety of tools, arranged hierarchically to reflect how surgical tools are organised in real-world conditions. If you do find our dataset useful, please cite our papers in your work: Rodrigues, M., Mayo, M, and Patros, P. (2022). OctopusNet: Machine Learning for Intelligent Management of Surgical Tools. Published in ���Smart Health���, Volume 23, 2022. https://doi.org/10.1016/j.smhl.2021.100244 Rodrigues, M., Mayo, M, and Patros, P. (2021). Evaluation of Deep Learning Techniques on a Novel Hierarchical Surgical Tool Dataset. Accepted paper at The 2021 Australasian Joint Conference on Artificial Intelligence. 2021. To be Published in Lecture Notes in Computer Science series. Rodrigues, M., Mayo, M, and Patros, P. (2021). Interpretable deep learning for surgical tool management. In M. Reyes, P. Henriques Abreu, J. Cardoso, M. Hajij, G. Zamzmi, P. Rahul, and L. Thakur (Eds.), Proc 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2021) LNCS 12929 (pp. 3-12). Cham: Springer.

Nature Medicine CiteScore 2024-2025 - ResearchHelpDesk - Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine on the basis of its originality, timeliness, interdisciplinary interest and impact on improving human health. Nature Medicine also publishes commissioned content, including News, Reviews and Perspectives, aimed at contextualizing the latest advances in translational and clinical research to reach a wide audience of M.D. and PhD readers. All editorial decisions are made by a team of full-time professional editors. Nature Medicine publishes research that addresses the needs and goals of contemporary medicine. Original research ranges from new concepts in human biology and disease pathogenesis to robust preclinical bases for new therapeutic modalities and drug development to all phases of clinical work, as well as innovative technologies aimed at improving human health. Current areas of interest also include, but are not limited to: Gene and cell therapies Clinical genomics Regenerative medicine High-definition medicine Effects of the environment in human health Artificial intelligence in health care Smart wearable devices Early disease diagnosis Microbiome Aging Nature Medicine also publishes Reviews, Perspectives and other content commissioned from leading scientists in their fields to provide expert and contextualized views of the latest research driving the progress of medicine. The Magazine section is editorially independent and provides topical and timely reporting of upcoming trends affecting medicine, researchers and the general audience.

Key assumptions of the decision analytic model.

Nature Medicine Acceptance Rate - ResearchHelpDesk - Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine on the basis of its originality, timeliness, interdisciplinary interest and impact on improving human health. Nature Medicine also publishes commissioned content, including News, Reviews and Perspectives, aimed at contextualizing the latest advances in translational and clinical research to reach a wide audience of M.D. and PhD readers. All editorial decisions are made by a team of full-time professional editors. Nature Medicine publishes research that addresses the needs and goals of contemporary medicine. Original research ranges from new concepts in human biology and disease pathogenesis to robust preclinical bases for new therapeutic modalities and drug development to all phases of clinical work, as well as innovative technologies aimed at improving human health. Current areas of interest also include, but are not limited to: Gene and cell therapies Clinical genomics Regenerative medicine High-definition medicine Effects of the environment in human health Artificial intelligence in health care Smart wearable devices Early disease diagnosis Microbiome Aging Nature Medicine also publishes Reviews, Perspectives and other content commissioned from leading scientists in their fields to provide expert and contextualized views of the latest research driving the progress of medicine. The Magazine section is editorially independent and provides topical and timely reporting of upcoming trends affecting medicine, researchers and the general audience.

✅ Nature Medicine Subscription Price - ResearchHelpDesk - Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine on the basis of its originality, timeliness, interdisciplinary interest and impact on improving human health. Nature Medicine also publishes commissioned content, including News, Reviews and Perspectives, aimed at contextualizing the latest advances in translational and clinical research to reach a wide audience of M.D. and PhD readers. All editorial decisions are made by a team of full-time professional editors. Nature Medicine publishes research that addresses the needs and goals of contemporary medicine. Original research ranges from new concepts in human biology and disease pathogenesis to robust preclinical bases for new therapeutic modalities and drug development to all phases of clinical work, as well as innovative technologies aimed at improving human health. Current areas of interest also include, but are not limited to: Gene and cell therapies Clinical genomics Regenerative medicine High-definition medicine Effects of the environment in human health Artificial intelligence in health care Smart wearable devices Early disease diagnosis Microbiome Aging Nature Medicine also publishes Reviews, Perspectives and other content commissioned from leading scientists in their fields to provide expert and contextualized views of the latest research driving the progress of medicine. The Magazine section is editorially independent and provides topical and timely reporting of upcoming trends affecting medicine, researchers and the general audience.

Context

A rapid march towards several smart health programs that are available online in the Hindi language, namely- https://www.onlymyhealth.com/hindi.html; https://pmsma.nhp.gov.in/; https://play.google.com/store/apps/details?id=com.knowledgeworld.HealthTipsHindi necessitates an emergence of the Hindi Health Data (HHD) corpus.

Content

HHD corpus is crawled using python 2.7.11 from Indian websites and four gazetteer lists- Person, Disease, Consumable and Symptom are detailed in our published research papers. (please refer ### Acknowledgements)

Acknowledgements

Special thanks goes to Dr. Anuja Arora, Associate Professor, CSE & IT, Jaypee Institute of Information Technology, Noida, India; Prof. Devendra K. Tayal, Dean (A& R), Indira Gandhi Delhi Technical University for Women, New Delhi, India.

Citations- 1. Jain, A., and Arora, A. Named Entity Recognition in Hindi Using Hyperspace Analogue to Language and Conditional Random Field. Pertanika Journal of Science and Technology, UPM, vol. 26, no. 4, pp. 1801-1822, 2018. 2. Jain, A., Tayal, D.K., and Arora, A. OntoHindi NER- An Ontology Based Novel Approach For Hindi Named Entity Recognition. International Journal of Artificial Intelligence, vol. 16, no. 2, pp. 1-36, 2018.

Inspiration

HHD corpus can help researchers to upgrade their research in the Hindi language while utilizing the health related entities. Some of these entities are available in a ready made mode within the corpus such as Disease while others need to be explored such as Diagnosis. In addition to the Named Entity Recognition, the corpus can be useful to perform various other Natural Language Processing tasks such as Question Answering, Co-reference Resolution, Parsing and many more.

Introduction: Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities. Methods: CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants’ homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing. Results: The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (n = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160–169 min), physical mobility (e.g., mean daily transitions between rooms = 96–155), sleep (e.g., mean nightly sleep duration = 6.3–7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15–45%) were collected across cohorts. Conclusion: The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.

In the environment of big data of the Internet of Things, smart healthcare is developed in combination with cloud computing. However, with the generation of massive data in smart healthcare systems and the need for real-time data processing, traditional cloud computing is no longer suitable for resources-constrained devices in the Internet of Things. In order to address this issue, we combine the advantages of fog computing and propose a cloud-fog assisted attribute-based signcryption for smart healthcare. In the constructed “cloud-fog-terminal” three-layer model, before the patient (data owner)signcryption, it first offloads some heavy computation burden to fog nodes and the doctor (data user) also outsources some complicated operations to fog nodes before unsigncryption by providing a blinded private key, which greatly reduces the calculation overhead of resource-constrained devices of patient and doctor, improves the calculation efficiency. Thus it implements a lightweight signcryption algorithm. Security analysis confirms that the proposed scheme achieves indistinguishability under chosen ciphertext attack and existential unforgeability under chosen message attack if the computational bilinear Diffie-Hellman problem and the decisional bilinear Diffie-Hellman problem holds. Furthermore, performance analysis demonstrates that our new scheme has less computational overhead for both doctors and patients, so it offers higher computational efficiency and is well-suited for application scenarios of smart healthcare.

Colorectal cancer (CRC) is one of the significant threats to public health and the sustainable healthcare system during urbanization. As the primary method of screening, colonoscopy can effectively detect polyps before they evolve into cancerous growths. However, the current visual inspection by endoscopists is insufficient in providing consistently reliable polyp detection for colonoscopy videos and images in CRC screening. Artificial Intelligent (AI) based object detection is considered as a potent solution to overcome visual inspection limitations and mitigate human errors in colonoscopy. This study implemented a YOLOv5 object detection model to investigate the performance of mainstream one-stage approaches in colorectal polyp detection. Meanwhile, a variety of training datasets and model structure configurations are employed to identify the determinative factors in practical applications. The designed experiments show that the model yields acceptable results assisted by transfer learning, and highlight that the primary constraint in implementing deep learning polyp detection comes from the scarcity of training data. The model performance was improved by 15.6% in terms of average precision (AP) when the original training dataset was expanded. Furthermore, the experimental results were analysed from a clinical perspective to identify potential causes of false positives. Besides, the quality management framework is proposed for future dataset preparation and model development in AI-driven polyp detection tasks for smart healthcare solutions.

Strategies used to address barriers and facilitate mental health services use based on Andersen’s modified Behavioural model of Health Services Use.

Regression results of the impact of the SCC on supply of medical resources.

Nurses play a crucial role in the adoption and continued use of Electronic Health Records (EHRs), especially in developing countries. Existing literature scarcely addresses how personality traits and organisational support influence nurses’ decision to persist with EHR use in these regions. This study developed a model combining the Five-Factor Model (FFM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the impact of personality traits and organisational support on nurses’ continuance intention to use EHR systems. Data were collected via a self-reported survey from 472 nurses across 10 public hospitals in Jordan and analyzed using a structural equation modeling approach (Smart PLS-SEM 4). The analysis revealed that personality traits, specifically Openness, Experience, and Conscientiousness, significantly influence nurses’ decisions to continue using EHR systems. Furthermore, organisational support, enhanced by Performance Expectancy and Facilitating Conditions, positively affected their ongoing commitment to EHR use. The findings underscore the importance of considering individual personality traits and providing robust organisational support in promoting sustained EHR usage among nurses. These insights are vital for healthcare organisations aiming to foster a conducive environment for EHR system adoption, thereby enhancing patient care outcomes.

Relationship between impact and exposure: Percentage of Women in the Intervention Group with Desired Knowledge or Behavior at Endline, by the Intensity of their Exposure to SMART Activities.

Testing hypothesis and path coefficients for the structural model.

Antimicrobial susceptibilities of the most common isolates including the ESBL-producing ones for the National Institutes of Health and General Hospitals, from intra-abdominal infections, from SMART study in Mexico from 2009 to 2015.

Key themes arising from perspectives of trial staff and families regarding the smart inhaler and algorithm.

Distribution of isolates in the National Institutes of Health and General Hospitals, categorized by intra-abdominal infections, urinary-tract infections and unknown, from SMART study in Mexico between 2009 and 2015.