Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls

Abstract

This study explored using big data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents’ Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell (n = 13) and those who did not fall (n = 10). We analyzed associations between participants’ fall events (n = 69) and pre-fall changes in in-home gait speed and stride length (n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall (p < .0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. Results demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.

Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing

“…26 Similarly, the comparison group’s decline of almost 5cm in both right and left stride length compared to the intervention group’s decline of just over 1cm bilaterally suggests the comparison group is at greater risk of falling than the intervention group. 11 Prior research examining in-home gait parameters found that a one-week change in stride length of 0.0254m was associated with a 6.78 odds of falling in the next three weeks. These findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.…”

“…11,12,30,33,34 Ultimately with this new technology, the research team believes costly hospitalizations and relocation to assisted living or nursing homes can be reduced. Without new solutions to the old challenges of promoting health, independence, and function, the service demand of older adults, who will represent 20% of our population in 2030, 35 has the potential to overwhelm our health care system and our country’s economic future.…”

Prevalence of mobility disabilities in nursing home residents and the association with age, BMI, fall risk and social participation

“…[21] Preliminary results also indicate a relationship between mobility disability and the probability of a fall. [22][23][24] This phenomenon is particularly often reported in RCFs. [25] Nursing scientists have analyzed the severity of physical, psychological, and economic consequences of a fall of an older adult.…”

Chasing Feet in the Wild: A Proposed Egocentric Motion-Aware Gait Assessment Tool

“…The recent explosion of ambient sensors (e.g., motion capture sensors, force mats), smart phones, and wearable sensor systems (e.g., inertial measurement units, IMUs) have facilitated the emergence of new techniques to monitor gait and balance control in natural environments and during everyday activities [8,22,29]. Embedded into living environments, ambient third-person video (TPV) and depth cameras (e.g., Microsoft Kinect) have been investigated as means to extract gait parameters [14,10], detect episodes of freezing of gait in Parkinson's disease [5], detect falls, and longitudinal changes in the patient's mobility patterns [4,36,3]. While TPV systems have demonstrated potential to detect small changes over long periods (i.e., months to years), these approaches suffer from visual occlusions (e.g., furniture), difficulty handling multiple residents, and extraction of spatiotemporal parameters when the full-body view is unavailable.…”