Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable preventive and diagnostic support. This work introduces a unique technique to address stress detection as an anomaly detection problem that uses quantum hybrid support vector machines. With the help of a wearable smartwatch, we mapped baseline sensor reading as normal data and stressed sensor reading as anomaly data using cortisol concentration as the ground truth. We have used quantum computing techniques to explore the complex feature spaces with kernel-based preprocessing. We illustrate the usefulness of our method by doing experimental validation on 40 older adults with the help of the TSST protocol. Our findings highlight that using a limited number of features, quantum machine learning provides improved accuracy compared to classical methods. We also observed that the recall value using quantum machine learning is higher compared to the classical method. The higher recall value illustrates the potential of quantum machine learning in healthcare, as missing anomalies could result in delayed diagnostics or treatment.

CCTV and other kinds of security cameras have a strong Big Brother vibe, but for many of us that may be because we don't really understand or know how the footage they pick up ever gets used. Today, a startup called Spot AI that's built a system to help answer that question at least in part -- it provides a cloud-based analytics system that "reads" that footage to get insights about not just security, but also safety and operational activity -- is announcing $40 million in funding to grow. Scale Venture Partners is leading the round, with past backers Redpoint Ventures, Bessemer Venture Partners, and new investors StepStone Group and Modern Venture Partners also investing. This brings the total raised by Spot AI to $62 million. Spot AI, appropriately for a security camera company, existed in stealth for years before it came out into the public in 2021: at that point it had already raised $22 million.