The Dual Task Ball Balancing Test and Its Association With Cognitive Function: Algorithm Development and Validation.

Background: Dual task paradigms are thought to offer a quantitative means to assess cognitive reserve and the brain's capacity to allocate resources in the face of competing cognitive demands. The most common dual task paradigms examine the interplay between gait or balance control and cognitive function. However, gait and balance tasks can be physically challenging for older adults and may pose a risk of falls.

Estimating balance, cognitive function, and falls risk using wearable sensors and the sit-to-stand test.

The five times sit-to-stand test (FTSS) is an established functional test, used clinically as a measure of lower-limb strength, endurance and falls risk. We report a novel method to estimate and classify cognitive function, balance impairment and falls risk using the FTSS and body-worn inertial sensors. 168 community dwelling older adults received a Comprehensive Geriatric Assessment which included the Mini-Mental State Examination (MMSE) and the Berg Balance Scale (BBS).

Development of Data-Driven Metrics for Balance Impairment and Fall Risk Assessment in Older Adults.

Ageing incurs a natural decline of postural control which has been linked to an increased risk of falling. Accurate balance assessment is important in identifying postural instability and informing targeted interventions to prevent falls in older adults. Inertial sensor (IMU) technology offers a low-cost means for objective quantification of human movement.

Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson's Disease.

People with Parkinson's disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson's disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD.

Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning.

Assessment of health and physical function using smartphones (mHealth) has enormous potential due to the ubiquity of smartphones and their potential to provide low cost, scalable access to care as well as frequent, objective measurements, outside of clinical environments. Validation of the algorithms and outcome measures used by mHealth apps is of paramount importance, as poorly validated apps have been found to be harmful to patients. Falls are a complex, common and costly problem in the older adult population.

Reliability of inertial sensor based spatiotemporal gait parameters for short walking bouts in community dwelling older adults.

Background: When performing quantitative analysis of gait in older adults we need to strike a balance between capturing sufficient data for reliable measurement and avoiding issues such as fatigue. The optimal bout duration is that which contains sufficient gait cycles to enable a reliable and representative estimate of gait performance.

Research Question: How does the number of gait cycles in a walking bout influence reliability of spatiotemporal gait parameters measured using body-worn inertial sensors in a cohort of community dwelling older adults?

Methods: One hundred and fifteen (115) community dwelling older adults executed three 30-metre walk trials in a single measurement session.

Short Bouts of Gait Data and Body-Worn Inertial Sensors Can Provide Reliable Measures of Spatiotemporal Gait Parameters from Bilateral Gait Data for Persons with Multiple Sclerosis.

Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gait data collected in non-controlled settings are often fragmented and may not provide enough information for reliable measures. This paper evaluates a novel approach to (1) determine the effects of the length of the walking task on the reliability of calculated measures and (2) identify digital biomarkers for gait assessments from fragmented data.

Impact of Exercise Intervention in Parkinson's Disease can be Quantified Using Inertial Sensor Data and Clinical Tests.

Parkinson's Disease (PD) has the second-highest prevalence rate of all neurodegenerative disorders. It effects approximately 1% of the population over the age of 60, with this proportion rising further, in more elderly cohorts. PD manifests as several motor and non-motor disfunctions, which develop progressively over time.

Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors.

Falls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the "first fall", delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning).