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dc.contributor.author DR. SHOAB A.KHAB DR. QAISER CHAUDRY DR. USMAN AKRAM, NS Saad Bin Waheed NS Muhammad Musab Akram
dc.date.accessioned 2025-04-30T09:43:08Z
dc.date.available 2025-04-30T09:43:08Z
dc.date.issued 2018
dc.identifier.other DE-COMP-36
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/52774
dc.description SUPERVISORS DR. SHOAB A.KHAB DR. QAISER CHAUDRY DR. USMAN AKRAM en_US
dc.description.abstract Currently around 9% of the world’s population is over the age of 60 and this figure is only expected to rise as according to a study 2 Billion people will be over the age of 60 by 2050. This causes two major problems; Firstly, an ever increasing burden on the care-takers who’s slight delay in response might cost an elderly’s life. Cases that require critical response time includes a senior person falling, suffering from heart or an epilepsy attack etc. According to a research, due to injuries following a fall 50% of the elderly population are unable to live independently and 25% will die within 6 months. Secondly, with old age comes neurodegenerative movement disorders. These disorders include Parkinson’s Disease, Alzheimer's disease, Dystonia and Benign Essential Tremor and are very often misdiagnosed due to the overlapping symptoms and subjective neurological tests by the doctors. We’re solving these two primarily age-related problems by constantly monitoring the vitals of the elderly and alerting their care-taker in case of a medical emergency. Also, we’d be providing clinical support system to the doctors in the diagnosis of Parkinson’s Disease and other related disorders. We have created a smartwatch application that will constantly monitor the heart-rate and activity levels of the elderly and in case of any irregularities generate a notification on the caretakers mobile. Furthermore, the patient will now have the ability to record the tremors while they are at their peak and upload the data to the cloud where machine learning algorithms would be applied on various features such as the signal’s frequency and mean standard deviation and the diseases will be diagnosed with a greater accuracy. en_US
dc.language.iso en en_US
dc.publisher College of Electrical & Mechanical Engineering (CEME), NUST en_US
dc.title Elderly Buddy en_US
dc.type Project Report en_US


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