Smart home technologies are increasingly used worldwide to monitor and control various aspects of daily life. However, a growing concern has emerged regarding the impact of these devices on domestic workers, who often find themselves under constant surveillance.

Researchers from King’s College London have conducted a study that reveals the unsettling truth about smart home devices being used to monitor domestic workers in China. The study, which involved interviews with 26 domestic workers and five recruitment agencies, highlights how employers are using these technologies to exert control over their employees, undermining their privacy and safety.

The researchers found that many domestic workers felt like they were under constant observation, with cameras and sensors installed in every room of the smart home. Some reported not being informed about the presence of cameras, while others discovered them hidden in bookshelves or disguised as other devices.

The constant feeling of being watched had a profound impact on the mental wellbeing of these domestic workers. Many felt uneasy, anxious, and even trapped in their own homes. The researchers concluded that this level of surveillance amounts to mental abuse, undermining trust and affecting workplace interactions.

Furthermore, the study revealed how smart home technologies exacerbate existing power imbalances between workers and employers. Employers can use these devices to monitor workers’ performance, track their movements, and even make decisions about their employment based on data collected from the devices.

The researchers highlighted that while many domestic workers recognized the need for safety, especially when caring for babies, the absence of clear communication from employers about the purpose of monitoring sparked distrust and discomfort. They recommended that domestic worker agencies integrate privacy education into training programmes for workers to understand their rights and establish transparent communication and contractual agreements regarding surveillance practices.

The study was conducted by researchers from King’s College London, along with collaborators from the University of St Andrews and the China Academy of Art, Hangzhou. The findings suggest that this issue is not unique to China but may be affecting migrant domestic workers worldwide.

In conclusion, the use of smart home technologies to monitor domestic workers raises significant concerns about their safety and privacy. The researchers’ recommendations provide a starting point for policymakers and agencies to address these challenges and ensure that domestic workers are protected from exploitation. As technology continues to advance, it is crucial that we prioritize human rights and dignity in the development and implementation of smart home devices.

The COVID-19 pandemic has brought to the forefront the critical importance of thorough disinfection, particularly within hospital environments. However, traditional manual disinfection methods have inherent limitations, including labor shortages due to physical fatigue and risk of exposure to pathogens, inconsistent human performance, and difficulty in reaching obscured or hard-to-reach areas.

To address these challenges, a team of researchers from Pohang University of Science and Technology (POSTECH) has developed an “Intelligent Autonomous Wiping and UV-C Disinfection Robot” that can automate hospital disinfection processes. This innovative robot is capable of navigating through hospital environments and performing disinfection tasks with precision and consistency.

The key feature of this robot is its dual disinfection system, which combines physical wiping and UV-C irradiation to effectively remove contaminants from surfaces. The robotic manipulator uses a wiping mechanism to physically clean high-touch areas, while the UV-C light ensures thorough disinfection of hard-to-reach corners and narrow spaces.

Real-world testing at Pohang St. Mary’s Hospital validated the robot’s performance, with bacterial culture experiments confirming its effectiveness in disinfecting surfaces. Repeated autonomous operations were carried out to verify its long-term usability in clinical settings.

The significance of this technology lies in its ability to automate time-consuming and repetitive disinfection tasks, allowing healthcare professionals to devote more attention to patient care. Additionally, the robot’s precision control algorithms minimize operational failures, while its integration with a self-sanitizing station and wireless charging system ensures sustained disinfection operations.

Professor Keehoon Kim emphasized that despite COVID-19 transitioning into an endemic phase, it remains essential to prepare for future pandemics by advancing this disinfection robot technology beyond hospitals to public facilities, social infrastructures, and everyday environments to further reduce infection risks. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT).

Early Cancer Detection: New Algorithms Revolutionize Primary Care

Two groundbreaking predictive algorithms have been developed to help General Practitioners (GPs) identify patients who may have undiagnosed cancer, including hard-to-detect liver and oral cancers. These advanced models use information about a patient’s health conditions and simple blood tests to accurately predict their chances of having an undiagnosed cancer.

The National Health Service (NHS) currently uses algorithms like the QCancer scores to combine relevant patient data and identify individuals at high risk of having undiagnosed cancer, allowing GPs and specialists to call them in for further testing. Researchers from Queen Mary University of London and the University of Oxford have created two new algorithms using anonymized electronic health records from over 7.4 million adults in England.

The new models are significantly more sensitive than existing ones, potentially leading to better clinical decision-making and earlier cancer diagnosis. Crucially, these algorithms incorporate the results of seven routine blood tests as biomarkers to improve early cancer detection. This approach makes it easier for patients to receive treatment at much earlier stages, increasing their chances of survival.

Compared to the QCancer algorithms, the new models identified four additional medical conditions associated with an increased risk of 15 different cancers, including liver, kidney, and pancreatic cancers. The researchers also found two additional associations between family history and lung cancer and blood cancer, as well as seven new symptoms of concern (itching, bruising, back pain, hoarseness, flatulence, abdominal mass, dark urine) associated with multiple cancer types.

The study’s lead author, Professor Julia Hippisley-Cox, said: “These algorithms are designed to be embedded into clinical systems and used during routine GP consultations. They offer a substantial improvement over current models, with higher accuracy in identifying cancers – especially at early, more treatable stages.”

Dr Carol Coupland, senior researcher and co-author, added: “These new algorithms for assessing individuals’ risks of having currently undiagnosed cancer show improved capability of identifying people most at risk of having one of 15 types of cancer based on their symptoms, blood test results, lifestyle factors, and other information recorded in their medical records.”