SUMMARY 

• Incorrect walker use is associated with increased fall risk in older adults, but little is being done to address the issue.
• Transitional movements such as turning can be a vital indicator of mobility quality, with longer turning durations being an indicator of higher fall risk.
• Our product, StrideTech Go, measures the two most common methods of incorrect walker use; excessive weight-bearing on the handles (measured as left- and right-hand force on the left and right-hand handles) and excessive distance between the user and the device (measured as the distance between the user’s hip and the frame of the walker) 
• With StrideTech Go, we can use our hip-distance measurements to determine 1. If someone is turning, 2. The duration of their turn and, 3. How much their hip-distance deviates from their baseline during a turn
• With StrideTech Go, we can use our left- and right-hand force measurements to determine asymmetrical gait

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‍INTRODUCTION

Stride Tech Medical Inc.’s mission is to prevent falls. Seniors widely use walkers to maintain mobility while reducing the risk of falls. Despite the benefits, habitually poor walker use, marked by excessive weight-bearing on the walker handles and/or excessive distance between the user and the walker, can lead to muscle atrophy, poor posture, and falls. A widely publicized investigation in 2009 showed over 87% of severe falls with an assistive device occurred with a walker. They recommended increased time devoted to fitting and education on proper use. Eleven years later, most seniors still do not receive individualized fitting or training on how to use their walkers.

Our product, the StrideTech Go, is an attachable walker accessory which integrates sensors and biofeedback onto existing walkers to correct common misuses in real-time. Grip covers are embedded with sensors and Velcro over the handles of a walker. An additional sensor is mounted to the frame which measures the user’s hip distance from the frame. The grip covers vibrate if the sensors detect either of the two primary indicators of walker misuse:

• Excessive weight load through the handles
• Excessive distance between the frame of the walker and the user

StrideTech Go is the first commercial product to help fill the urgent need for long-term walker use training. This white paper will outline the technical background and testing done to establish efficacy and briefly outline next steps and improvements. 

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PROTOCOL

StrideTech defines short-term efficacy as the ability to see changes in StrideTech Go measurements of walker use in a single product testing session. The data presented was collected from a walker repair event held at a local senior living community. The user was asked to complete a baseline in which they walked the length of the room to a marker, turned around, and walked back to the start position. Testers repeated the back and forth walk three times. They were NOT given StrideTech Go feedback. This baseline data is crucial, as it allows StrideTech to assess the range of weight and hip distances measured across different older adults for both straight-line walking and common transitional movements like turning. This reaffirms the sensors have the adequate range and sensitivity for this population. 

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Additionally, the entirety of the baseline walk was filmed. The timing and direction of each turn taken by the user to switch walking directions were recorded. The user was not instructed on which direction to turn around (right or left).

Above is a graph of the hip distance (in inches) against the time of the baseline trial (in minutes). What is especially exciting to see is the dramatic and clear sudden drop, then an increase in hip distance that repeats periodically. Each of these hip-distance patterns is highlighted in red circles. Cross referencing the data and video timestamps, each dramatic change in hip distance were matched with the user turning 180°. This is incredibly exciting and valuable, as it allows the StrideTech team to discern between random, noisy data, and common transitional behaviors like turning that may skew data sets.

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In fact, turning can be an indicator of balance ability. It has been reported that older adults with high fall risk take a longer time to turn.^1,2,3,4.Reporting the data measured behind each turn, such as total time, range, and frequency, may be additionally helpful to users and/or PTs who incorporate turning into rehabilitation or strengthening exercises. Decreases in time of turning could be an additional indicator of improved mobility. 

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For this user, her fastest turning time was approximately 7.8 seconds and her slowest turning time was approximately 11.4 seconds. For reference, in one study examining the straight-line walking and transitional movements performed in a Timed Up and Go (TUG), a common clinical tool to measure fall risk), the median time it took for healthy older adults to complete one turn was .82 seconds ⁵. Her average hip distance during straight line walking was about 20.2 inches. During turning, her largest decrease in hip distance was 5.4 inches., and her smallest decrease in hip distance was 2 inches. 

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It is additionally interesting to see the steady increase of hip distance in between each turning event.  Visually, a pattern of walking behavior can be seen that indicates this user was slowly increasing the distance between her hips and the device over time. Each time she turned, she pivoted her walker around herself, and essentially “reset” her hip distance. Future studies allowing for longer straight-line walking will be incredibly valuable to assess if older adults increase their hip-distance between the walker over time. 

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Case Study 1 establishes StrideTech Go’s ability to identify patterns in hip distance data as observable events like turning. This ability is vital to the StrideTech Go feedback algorithm, as the ability to discount common behaviors like turning, stepping up, stepping down, and opening and closing doors that may drastically affect hip-distance data will allow for more accurate feedback on normal walking behavior. Refined metrics (duration, range, frequency, etc.) on common transitional patterns like turning could also provide invaluable insights to PTs on their patient’s daily rehabilitation progression.

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The above graph shows the left- and right-hand force (in lbf) placed on walker handles as the user completed her baseline walk. A clear overall difference in left (gray markers) and right (orange markers) can be seen. Her left-hand force on the left handle averages about 18 lbf, with a small variance. Her right-hand force on the right handle averages about 31 lbf, with a large variance. This suggests asymmetrical loading through the handles, dominated by her right side. The video footage of her walking confirms a dominant favoring of her right side. Moreover, dips and increases in load-bearing on both sides also align with the timestamped turning events, suggesting turning may result in dips in load-bearing on both sides.

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These case studies are a highlight of the incredible efficacy and opportunity StrideTech Go offers. The ability to discern and potentially disregard everyday behaviors, like turning, which may negatively affect a user’s StrideTech Go data and feedback is vital to a product that effectively gives feedback at the appropriate times. Further, as PTs and users incorporate more motions of daily living, like turning or stepping, into exercise, the ability to see improvement in these motions could be vital to PT exercise and rehabilitation. 

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FUTURE WORK 

More data and more testing are needed to explore the exciting potential of the StrideTech Go. Future work will call for longer straight-line walking (to assess drift in the hip distance over time), figure 8 walking formations (to ensure every participant is turning in the same direction and completes both a right and left-hand turn), and more subjects.

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1. ^{Imms FJ, Edholm OG: Studies of gait and mobility in the elderly. Age Ageing, 1981, 10: 147–156.}
2. ^{Lipsitz LA, Jonsson PV, Kelley MM, et al. : Causes and correlates of recurrent falls in ambulatory frail elderly. J Gerontol, 1991, 46: M114–M122.}
3. ^{Thigpen MT, Light KE, Creel GL, et al. : Turning difficulty characteristics of adults aged 65 years or older. Phys Ther, 2000, 80: 1174–1187.}
4. ^{Kurosawa C, Shimazu N, Yamamoto S. Where do healthy older adults take more time during the Timed Up and Go test?. J Phys Ther Sci. 2020;32(10):663-668. doi:10.1589/jpts.32.663}
5. ^{Kurosawa C, Shimazu N, Yamamoto S. Where do healthy older adults take more time during the Timed Up and Go test?. J Phys Ther Sci. 2020;32(10):663-668. doi:10.1589/jpts.32.663}