Exploring the interaction of knee and ankle component use on mobility test performance in people with unilateral transfemoral amputation

Barnett, Cleveland; Hughes, Liam D; Sullivan, Amy E.; Strutzenberger, Gerda; Levick, Jodie L; Bisele, Maria; Asha, Alan R. De

doi:10.1097/pxr.0000000000000042

Cited by 3 publications

(2 citation statements)

References 32 publications

(40 reference statements)

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“…Results from this study are particularly relevant to those who want to measure locomotion in a real-world setting for a prolonged period. As locomotion and gait speed is used as a marker for and can be used to inform prescribing assistive devices [46], knowledge of how an individual changes through locomotion types over several weeks has the potential to alter clinical practices and prescriptions. In addition, the ability to use one sensor in a minimum of three attachment locations allows clinicians and patients to work together to find an area that is the most comfortable for the user.…”

Section: Discussionmentioning

confidence: 99%

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach

Hughes,

Bencsik,

Bisele

et al. 2023

Sensors

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Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category’s cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.

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Section: Discussionmentioning

confidence: 99%

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach

Hughes,

Bencsik,

Bisele

et al. 2023

Sensors

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“…There is published evidence that hydraulic ankles present increased ground clearance during swing [27,28] and enhanced standing stability [29], compared to feet that are rigidly-attached at the ankle. More recently, some researchers have investigated how the combinations of advanced prosthetic knees and ankles work together, suggesting the combination of the two technologies may have biomechanical advantages for balance [29], mobility [30] and navigating everyday, environmental barriers, such as stairs and slopes [30]. It is possible that these characteristics may have provided the MPK users with further protection from trips and falls, compared to non-MPK users that did not have hydraulic ankles, but it is not possible to draw definitive conclusions from the current analyses.…”

Section: Limitationsmentioning

confidence: 99%

Can microprocessor knees reduce the disparity in trips and falls risks between above and below knee prosthesis users?

et al. 2022

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While all lower limb prosthesis walkers have a high risk of tripping and/or falling, above knee prosthesis users are reported to fall more frequently. Recognising this, engineers designed microprocessor knees (MPK) to help mitigate these risks, but to what extent these devices reduce this disparity between above and below knee users is unclear. A service review was carried out in a prosthetic limb centre regarding the frequency of trips and falls in the previous four weeks. Data from unilateral, community ambulators were extracted. Ordered logistic regressions were applied to investigate whether MPKs mitigated the increased risk of trips and falls for prosthetic knee users, compared to below knee prosthesis users. Socio-demographics (sex, age), prosthesis (prosthesis type, years of use), health (comorbidities, vision, contralateral limb status, medication), and physical function (use of additional walking aids, activity level) were included as covariates. Of the 315 participants in the analysis, 57.5% reported tripping and 20.3% reported falling. Non-microprocessor prosthetic knee (non-MPK) users were shown to trip significantly more than below knee prosthesis users (OR = 1.96, 95% CI = 1.17–3.28). Other covariates showing a significant association included contralateral limb injuries (OR = 1.91, 95% CI = 1.15–3.18) and using an additional walking aid (OR = 1.99, 95% CI = 1.13–3.50). Non-MPK users were also shown to fall significantly more than below knee prosthesis users (OR = 3.34, 95% CI = 1.73–6.45), with no other covariates showing a significant association. MPK users did not show an increased frequency of trips (OR = 0.74, 95% CI = 0.33–1.64) or falls (OR = 0.34, 95% CI = 0.18–2.62), compared to below knee prosthesis users. Of those who tripped at least once in the previous four weeks, those using a non-MPK (OR = 2.73, 95% CI = 1.30–5.74) presented an increased frequency of falling. These findings provide evidence to suggest that the use of MPKs reduces the difference in falls risk between above knee and below knee prosthesis users, providing justification for their provision.

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Use of Physical Activity Measures in Rehabilitation Interventions Following Lower Extremity Amputation

Murray

McGinty

2023

Curr Phys Med Rehabil Rep

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Purpose of Review This systematic review aims to evaluate physical performance outcome tools that are used most frequently to assess rehabilitation interventions. The scope of this paper focused on outcomes used with established lower limb amputees when assessing interventions such as exercise programs or changes in prescription published in the last 5 years. Recent Findings The most recorded outcome measures used across all the papers were timed walk tests and the Activity Balance Confidence Scale. Many outcomes did not produce statistically significant results with established amputee cohorts. Understanding the minimal important clinical difference is key. Summary The use of outcome measures is essential. Training and education are likely to increase the use of outcome measures. Quality of life measures are important in conjunction with physical outcomes. Simple timed walk tests are commonly used. These are in general easy to administer requiring a small space, limited equipment, and a short time frame.

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Exploring the interaction of knee and ankle component use on mobility test performance in people with unilateral transfemoral amputation

Cited by 3 publications

References 32 publications

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach

Can microprocessor knees reduce the disparity in trips and falls risks between above and below knee prosthesis users?

Use of Physical Activity Measures in Rehabilitation Interventions Following Lower Extremity Amputation

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