# Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults
## 1 Introduction  
Falls are one of the most important factors threatening healthy aging. They are a major cause of injury and mortality in older adults. Every year, an estimated 30-40% of adults over the age of 65 experience at least one significant fall event, a third of which result in death [1]. The rate of fall increases to over 50% over the age of 80 [2]. Falls often lead to severe injuries, resulting in the degradation of physical functioning, affecting the individual's physical and mental health, and causing a loss of independence and a reduction in quality of life. In addition, after emergency treatment, falls are typically followed by a lengthy regime of rehabilitation. Due to limited medical resources, patients are often discharged early from the medical institute to complete rehabilitation in the home setting. This is often ineffective, as face-to-face treatments in local clinics and physiotherapy centers cannot support daily visits, which are necessary for effective and efficient rehabilitation. Furthermore, frequent assessments of balance and the risk of fall are necessary during rehabilitation in order to assess progress and physical improvement, as well as to individualize and adapt the rehabilitation exercises to the needs of the patient. Thus, two major problems must be overcome: (1) the early and effective detection of those at risk of fall, for example, via wide screening of the community, and (2) an effective balance and fall risk assessment that can be administered frequently and efficiently during rehabilitation. The early and effective detection of those at risk of fall, as well as their referral to early preventive treatment, has the potential to address the serious global health problem offalls [3]. Indeed, worldwide calls for research have been initiated for fall risk assessment and prevention [4]. Similarly, effective and frequent assessments of balance and the risk of fall can improve and guide rehabilitation treatments. There are numerous screening tools available for healthcare providers to assess the risk of falls: self-report questionnaires and tests of postural stability, functional strength, and walking [5]. However, there is no single uniform gold standard measure in the literature for fall risk assessment [6]. Furthermore, existing protocols are time consuming and designed as face-to-face procedures typically used in hospitals following medical treatment and before discharge. Specifically, access to fall risk assessment via a remote platform, rather than face to face, may serve to address the rising concern associated with falls in the elderly population. Remote assessment has the potential benefit of allowing for a wide screening of the elderly community and the early detection of the risk of fall, as well as allowing for efficient and recurring assessments during rehabilitation. Concurrently, this would allow for the better use of the limited resources of medical caregivers and medical institutions. Nonetheless, some of the balance assessment tasks cannot be performed in a home environment since they require contact with an assessor or a wide space for walking. These tasks are not feasible for remote testing. In this study, we aim to alleviate two concerns in implementing the wide screening and remote administration of fall risk and balance assessments: (a) reduce the length of screening, which is very lengthy for a full assessment, and (b) ensure the safety of the individual undergoing a balance assessment in a remote setting by relying only on feasible tasks. In this study, we use a novel machine learning approach to create a testing scheme that is adapted to each individual, creating a shortened test while retaining a high accuracy of scoring. Furthermore, we show that the adaptive test is applicable even when restricted to tasks that are feasible for remote testing. We show that, although fewer tasks are performed by the subject during the assessment, the accuracy in predicting the score assigned by the physiotherapists is very high.