Introduction
More seniors live alone than any other age group (Zhu, Sheng 2009). The aging and elderly often live without anyone like spouses and children in apartments, homes, and assisted living facilities. Elderly people could use more assistance with the duties and responsibilities that are required of living alone. Helping elderly people to live a life that features more comfort and assistance would be of great advantage and profit. Robots and sensors have proved to be the cutting-edge of assisted living technology. According to Zhu and Sheng, wearable sensor based activity has been gaining in popularity (2009). Force sense resistors and fabric stretch sensors can be easily integrated into clothing. (Zhu, Sheng 2009).
Machine algorithms have been used currently for human activity recognition. The combination of different machine algorithms is often the best way as it results in a more useful system (Zhu, Sheng 2009). These machine algorithms are used to detect the actions of a human like sitting down, standing up, and falling. In this manner, other robots or people can be informed if an elderly person falls or has an accident in their home.
Senior citizens and the elderly currently suffer from social and physical isolation and chronic effects that include cognitive decline, mild dementia, low activity levels, and poor mood states. (Agnes, 2010) There are currently systems in process that will fight things like cognitive decline, mild dementia, low activity levels, and poor mood states. The lessening of social isolation is also being assisted with Facebook applications for home assistant technologies.
The SAIL System
SAIL stands for smart assisted living system (Zhu, Sheng 2009). SAIL is an assisted living technology. The SAIL system consists of a body sensor network (BSN), a companion robot, a Smartphone, and a remote health provider (Zhu, Sheng 2009). The robot ascertains human intentions and situations from the motion data and vital signs of the older or elderly person. The robot must be able to recognize the person’s activities so that it can realize when or if the patient has a sudden fall to the floor (Zhu, Sheng 2009). SAIL is 2-step human daily activity recognition method combining the neural networks and the hidden Markov models (Zhu, Sheng 2009). The user of a SAIL system would use wearable sensors for human daily activity recognition. The sensors provide 3D acceleration, angular velocity, magnetic data, and temperature. A PDA, Wi-Fi, and a desktop are used together to realize when different activities take place(Zhu, Sheng 2009).Neural networks can spot the difference between moving activity from stationary activity(Zhu, Sheng 2009). Using computer algorithms, hand gestures can even be processed. (Zhu, Sheng 2009). When the elderly person falls down or has an accident, a companion robot can be communicated with to help the person. (Zhu, Sheng 2009). This may save the elderly person’s life.
Robots
Prototypes are being designed to provide automated assistance to the elderly at home. This market will grow larger and larger as people live longer (Horowitz, 2010). The vision for one such robot, Kompai, is one in which family members would call the robot via Skype. The robot then utilizes ultrasonic sensors to detect the location of the person being called and navigate to that person. The person then answers the Skype videoconference call via Kompai’s tablet PC and Webcam. The robot might also be used with Facebook, MySpace, or some other social network. Interactive speech recognition would be available to assist elderly and dependent people (Horowitz, 2010). Kompai could also store a person’s daily schedule and shopping lists. Online calendars and weather could be accessed (Horowitz, 2010). Robosoft wants to partner with companies that make wireless physiological sensors. These sensors could be worn by a robot’s owner and the sensors could communicate blood pressure, pulse, body temperature, and other data through Bluetooth to the robot. The robot could then pass this information along to the person’s doctor (Horowitz, 2010).
CareBot is another robot designed by the same company. Instead of an assisted living facility, the CareBot allows seniors to stay in their homes. Isolation is reduced for the users of this system by initiating videoconferencing sessions with family members. The user can also access information regarding daily tasks and access Web tools on the robot’s touch-screen (Horowitz, 2010). The CareBot prototype runs five to 12 software-based GeckoSavant artificial intelligence(AI) engines that run on netbook-size PCs. GeckoSavant relies on sensor fusion. Sensor fusion is a combination of multiple sensor systems such as vision and hearing that allow the robot to maintain awareness of its surroundings (Horowitz, 2010). These types of robots may help citizens to stay in their homes.
Agnes
Agnes is a system that is currently in development. Its areas of research include psychology of aging, activity detection, emotion recognition, social networking, and tangible and ambient interaction. Agnes uses the power of a dedicated social network to reduce feelings of loneliness and insecurity (Agnes, 2010). The combination of home-based ICT and social networks is central to the very idea of Agnes. These things are used to connect the elderly person living at home with their families, friends, and caretakers (Agnes, 2010). Agnes will utilize unobtrusive detection of user states and activities, a social networking technology platform, ambient devices, the needs of the older person, and the needs of the peole that care for them (Agnes, 2010).
Conclusion
One of the many hurdles for these futuristic caretakers is acceptance. Usually, elderly people using PCs or mobile phones will take in robots in their homes in the same manner. University of Louisville’s Robinson takes a longer view and expects it will take another 20 or years or so, when baby boomers are in their 80s, for the robots to be widely accepted in seniors’ homes (Horowitz, 2010). Once the acceptance of assisted living technology spreads, these ubiquitous assisted living robots and other technologies will be more common. Look for one in your neighborhood soon.
Works Cited:
Zhu, C., & Sheng, W. (2009). Human Daily Activity Recognition in Robot-assisted Living Using Multi-sensor Fusion. Retrieved November 27, 2010 from Computer Science Department, Oklahoma State University : http://www.cs.okstate.edu/tacs09/paper7.pdf.
Agnes User-sensitive Home-based Systems for Successful Ageing in a Networked Society. (2010). Retrieved November 27, 2010 from Agnes-aal: http://agnes-aal.eu/site/images/agnes_brochure_feb072010.pdf.
Horowitz, B. T. (2010). Cyber Care: Will Robots Help the Elderly Live at Home Longer? Retrieved November 27, 2010 from Scientific American: http://www.scientificamerican.com/article.cfm?id=robot-elder-care.
http://www.bukisa.com/articles/464631_the-future-of-human-computer-interaction-and-elderly-caretakers