Novel walking pole gait pattern improves activity in an older adult with chronic low back pain.

A 65-year-old woman with chronic low back pain participated in a 1-week community walking poles course. Although the participant received instruction in the standard Nordic walking method, she independently adopted a novel, modified, two-point gait pattern. Subsequently, her pain and activity tolerance using walking poles were monitored at 6 and 12 months. The participant ambulated two times the distance and reported lower ratings of perceived exertion and pain at 6-month and 12month follow-ups when walking with poles compared with walking without poles. This case highlights the potential effect of respecting patient preference within the clinical decision-making model. Doing so empowered a participant with chronic low back pain to adopt a novel, self-selected gait pattern and improve her short-term and long-term outcomes associated with chronic musculoskeletal disease.

Smart Carpet: Developing a sensor system to detect falls and summon assistance.

Cognitive deficits experienced by older adults with dementia limit use of wearable devices (necklaces or bracelets) that summon assistance after the older adult falls. To use these wearable devices, older adults must choose to wear them, remember how to use them, and be conscious after falling. Devices such as the Smart Carpet substitute pre-programmed or automatic functions for functions requiring deliberation and decision. After development of a Smart Carpet prototype, 11 volunteers participated in tests to measure sensitivity of sensors embedded in the Smart Carpet. The embedded sensors were not perceptible to the volunteers as they walked across the Smart Carpet and successfully detected gait characteristics. Findings confirmed the feasibility of fall detection. Measurements obtained of gait characteristics will be used in development of more advanced versions of the Smart Carpet.

Investigation of gait features for stability and risk identification in elders.

Today, eldercare demands a greater degree of versatility in healthcare. Automatic monitoring devices and sensors are under development to help senior citizens achieve greater autonomy, and, as situations arise, alert healthcare providers. In this paper, we study gait patterns based on extracted silhouettes from image sequences. Three features are investigated through two different image capture perspectives: shoulder level, spinal incline, and silhouette centroid. Through the evaluation of fourteen image sequences representing a range of healthy to frail gait styles, features are extracted and compared to validation results using a Vicon motion capture system. The results obtained show promise for future studies that can increase both the accuracy of feature extraction and pragmatism of machine monitoring for at-risk elders.

Tracking Exercise Motions of Older Adults Using Contours.

In this paper we describe the development of a novel markerless motion capture system and explore its use in documenting elder exercise routines in a health club. This system uses image contour tracking and swarm intelligence methods to track the location of the spine and shoulders during three exercises - treadmill, exercise bike, and overhead lateral pull-down. Validation results show that our method has a mean error of approximately 2 degrees when measuring the angle of the spine or shoulders relative to the horizontal. Qualitative study results demonstrate that our system is capable of providing important feedback about the posture and stability of elders while they are performing exercises. Study participants indicated that feedback from our system would add value to their exercise routines.