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This article provides tips to reduce electricity consumption and save money on bills during the summer heatwave. Here are some key takeaways:
Power down electronics completely; don't rely on standby mode. Use a power strip to easily turn off multiple appliances. Set your air conditioner between 24-26 degrees Celsius and use a timer. Replace old incandescent bulbs with LEDs. Regularly dust light fixtures for better light output. Consider using motion sensors or timers for lights. Keep your refrigerator away from heat sources and check the door seal for leaks.
Enter the Phigolf Golf Simulator. Pair the stick with an app to get access to real golf courses around the globe, but via the virtual world. The simulator gives users access to many golf courses with the ability to buy entry to others.
Frailty assessment is a critical approach to assessing the health status of older people. There is interest in the nature and degree of frailty in various physical, environmental, social, psychological, and physiological domains. The physical domain has proved to be the most important as the degree of frailty can be assessed objectively. Various clinical tools deployed by geriatricians in assessing frailty can be grouped into two categories of questionnaire-based and physical performance analysis. In the former approach, answers vary from double (yes or no) to multiple choices and are marked accordingly. Whereas, in performance analysis, the time taken by a subject to complete a physical task such as walking for 3 m is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not identify the kinematic characteristics of motion and its root causes of it. However, motion characteristics are crucial in measuring the degree of frailty. Overall, reliable and objective frailty assessment tool that can be adapted universally is unavailable. This study investigated the development of a quantitative, objective, non-invasive frailty measurement method that can assess frailty in older people. The frailty was assessed by measuring the body motion characteristic using an array of wearable inertial sensors embedded in a motion capture system and advanced machine learning methods. A set of experimental procedures was designed to assess the frailty in older people by performing various tasks such as “Pick-up an object from the floor,” “Sit to Stand,” and “Stand to Sit.” The data obtained through these activities were analysed, and features were extracted from various sensor data to create datasets for training algorithms. Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machine, Deep learning Model, and Gaussian Mixture Model were applied to the data set to assess and determine the frailty objectively. This study focused on a quantitative assessment of frailty without taking any age-related, cognitive, and pathological factors into consideration, as it is beyond the scope of this study. The results of developed algorithms were benchmarked against conventional clinical measuring tool, i.e., Rockwood Frailty Scale. The analysis of the frailty assessment by the proposed algorithms showed that these proposed methods could serve as an alternative objective frailty assessment tool to the existing subjective frailty measuring methods. The performance of various classifiers showed a significant difference between the non-frail and frail classes.
The global automotive sensor market is anticipated to register a CAGR of 6.6% during 2022-2030, accelerated by the wide adoption of ADAS features in...
Hand gesture recognition can be exploited to benefit ubiquitous applications using sensors. Currently, the inherent complexity of human physical activities makes it difficult to accurately recognize gestures with wearable sensors, especially in real time. To this end, a real-time hand gesture recognition system is presented in this paper. In particular, sliding window technology and y-axis threshold are used to detect intended gestures from a continuous data stream and then the segmented data are classified by applying a stacked Long Short-Term Memory (LSTM) model. After noise is removed, six-axis sensor data from wrist-worn devices are fed into the model without requiring feature engineering. We use twelve common hand gestures to evaluate the performance of our model. The experimental results demonstrate the feasibility of our proposed system with an accuracy of 99.8% on average. Our approach allows for an accurate and nonindividual hand gesture recognition. It holds potential to be integrated into a smart watch or other wearable devices for intuitive human computer interaction.
Senda Biosciences is developing drugs based on an understanding of intersystems biology—the way that humans interact with bacteria and plants. The startup, founded by Flagship Pioneering, has added $55 million to advance its three lead programs to clinical testing next year.
Glen Tullman’s newest healthcare navigation startup, Transcarent, raised another $58 million in funding. The company plans to use the funds to quickly build out its offering for employers.