Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.

Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings. However, there is a lack of studies examining accuracy in the real-world setting. In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. Five young adults and 19 older adults went about their daily activities while wearing tri-axial accelerometers. Older adults experienced 10 unanticipated falls during the data collection. Approximately 400 hours of activities of daily living were recorded. We employed a machine learning algorithm, Support Vector Machine (SVM) classifier, to identify falls and non-fall events. We found that our system was able to detect 8 out of the 10 falls in older adults using signals from a single accelerometer (waist or sternum). Furthermore, our system did not report any false alarm during approximately 28.5 hours of recorded data from young adults. However, with older adults, the false positive rate among individuals ranged from 0 to 0.3 false alarms per hour. While our system showed higher fall detection and substantially lower false positive rate than the existing fall detection systems, there is a need for continuous efforts to collect real-world data within the target population to perform fall validation studies for fall detection systems on bigger real-world fall and non-fall datasets.

Objective Identification of the factors that influence sedentary behaviour in older adults is important for the design of appropriate intervention strategies. In this study, we determined the prevalence of sedentary behaviour and its association with physical, cognitive, and psychosocial status among older adults residing in Assisted Living (AL). Methods Participants (n = 114, mean age = 86.7) from AL sites in British Columbia wore waist-mounted activity monitors for 7 consecutive days, after being assessed with the Timed Up and Go (TUG), Montreal Cognitive Assessment (MoCA), Short Geriatric Depression Scale (GDS), and Modified Fall Efficacy Scale (MFES). Results On average, participants spent 87% of their waking hours in sedentary behaviour, which accumulated in 52 bouts per day with each bout lasting an average of 13 minutes. Increased sedentary behaviour associated significantly with scores on the TUG (r = 0.373, p < 0.001) and MFES (r = −0.261, p = 0.005), but not with the MoCA or GDS. Sedentary behaviour also associated with male gender, use of mobility aid, and multiple regression with increased age. Conclusion We found that sedentary behaviour among older adults in AL associated with TUG scores and falls-related self-efficacy, which are modifiable targets for interventions to decrease sedentary behaviour in this population.

Falls are the number one cause of injury in older adults. Wearable sensors, typically consisting of accelerometers and/or gyroscopes, represent a promising technology for preventing and mitigating the effects of falls. At present, the goal of such "ambulatory fall monitors" is to detect the occurrence of a fall and alert care providers to this event. Future systems may also provide information on the causes and circumstances of falls, to aid clinical diagnosis and targeting of interventions. As a first step towards this goal, the objective of the current study was to develop and evaluate the accuracy of a wearable sensor system for determining the causes of falls. Sixteen young adults participated in experimental trials involving falls due to slips, trips, and "other" causes of imbalance. Three-dimensional acceleration data acquired during the falling trials were input to a linear discriminant analysis technique. This routine achieved 96% sensitivity and 98% specificity in distinguishing the causes of a falls using acceleration data from three markers (left ankle, right ankle, and sternum). In contrast, a single marker provided 54% sensitivity and two markers provided 89% sensitivity. These results indicate the utility of a three-node accelerometer array for distinguishing the cause of falls.

Falls are the number one cause of injury in older adults. An individual's risk for falls depends on his or her frequency of imbalance episodes, and ability to recover balance following these events. However, there is little direct evidence on the frequency and circumstances of imbalance episodes (near falls) in older adults. Currently, there is rapid growth in the development of wearable fall monitoring systems based on inertial sensors. The utility of these systems would be enhanced by the ability to detect near-falls. In the current study, we conducted laboratory experiments to determine how the number and location of wearable inertial sensors influences the accuracy of a machine learning algorithm in distinguishing near-falls from activities of daily living (ADLs).