The central nervous system (CNS) controls and coordinates functions of the human body and mind. Damage to the CNS causes several disorders such as cervical spondylosis, stroke, amongst others. These disorders deprive the abilities of a person needed for daily life. Rehabilitation is one of the key components in recovery of these abilities after CNS damage and Well-established and coordinated multidisciplinary efforts by specialized rehabilitation services are needed. However, such services are limited and inadequate in many developing countries. Also, effective rehabilitation requires the involvement of highly trained professionals. Shortage of such experts is also a serious problem in low to middle level income countries. Many individuals with disabilities depend on other people for daily activities. Therefore, researchers have developed many automated rehabilitation systems based on technology to address the lacuna of health care professionals who are trained to assist patients with disabilities. Such systems offer a number of advantages over conventional methods, including a standardized training environment and the ability to increase therapy intensity and dose, while reducing the physical burden on therapists. This paper provides a review of literatures on automated rehabilitation techniques developed for patients with CNS disorders. The literature reviewed in this paper suggests that for successful development of automated rehabilitation, a knowledge about the physiological basis of the recovery of function, employed is essential. A spirit of cooperation between physicians, health care professionals such as physiotherapists, clinical neuro rehabilitation scientists and information technology specialists is also necessary.

Cervical cancer is among the most commonly occurring cancer among women and claims a huge number of lives in low and middle-income countries despite being relatively easy to treat. Several studies have shown that public screening programs can bring down cervical cancer incidence and mortality rates significantly. While several screening tests are available, visual inspection with acetic acid (VIA) presents itself as the most viable option for low-resource settings due to the affordability and simplicity of performing the test. VIA requires a trained medical professional to interpret the test and is subjective in nature. Automating VIA using AI eliminates subjectivity and would allow shifting of the task to less trained health workers. Task shifting with AI would help further expedite screening programs in low-resource settings. In our work, we propose a lightweight deep learning algorithm that includes EfficientDet-Lite3 as the Region of Interest (ROI) detector and a MobileNet- V2 based model for classification. These models would be deployed on an android-based device that can operate remotely and provide almost instant results without the requirement of highly-trained medical professionals, labs, sophisticated infrastructure, or internet connectivity. The classification model gives an accuracy of 92.31%, a sensitivity of 98.24%, and a specificity of 88.37% on the test dataset and presents itself as a promising automated low-resource screening approach.