A Comparison of the Two-Minute Walk Test and Comprehensive High-level Activity Mobility Predictor (CHAMP) in People with a Leg Prosthesis

Objective To determine if the two-minute walk test (2MWT) could serve as an alternative measure of high-level mobility in lower limb prosthesis users when circumstances preclude administration of the Comprehensive High-level Activity Mobility Predictor (CHAMP). Design Cross-sectional study. Setting Indoor recreational athletic field and gymnasium Subjects Fifty-eight adult lower limb prosthesis users with unilateral or bilateral lower limb amputation who participate in recreational athletic activities. Intervention N/A Main Measures The 2MWT and CHAMP while using their preferred prosthesis(es) on an indoor artificial athletic field or hardwood gymnasium floor. Results Thirty-nine men and nineteen women with a median age of 38.3 years participated in the study. Most participants experienced amputation(s) due to trauma (62%) or tumor (10%) and were generally higher functioning (K4 (91.4%) and K3 (8.6%)). The median (range) score for the CHAMP was 23.0 points (1.5–33.5) and the mean ± standard deviation (range) 2MWT distance walked was 188.6 ± 33.9 m (100.2–254.3 m). The CHAMP demonstrated a strong positive relationship with 2MWT (r = 0.83, p < 0.001). The 2MWT distance predicted 70% of the variance in CHAMP score. Conclusions Although the 2MWT does not test multi-directional agility like the CHAMP, they were found to be highly correlated. If space is limited, the two-minute walk test can serve as an alternative measure for assessing high-level mobility capabilities in lower limb prosthesis users.

Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis

Objective. In order to study the motion recognition intention of lower limb prosthesis based on the CNN deep learning algorithm. Methods. A convolutional neural network (CNN) model was established to reconstruct the motion pattern. Before the movement mode of the affected side was converted, the sensor was bound to the healthy side. The classifier was employed to extract and classify the features, so as to realize the accurate description of the movement intention of the disabled. Results. The method proposed in this research can achieve 98.2% recognition rate of the movement intention of patients with lower limb amputation under different terrains, and the recognition rate can reach 97% after the pattern converted between the five modes was added. Conclusion. The deep learning algorithm that automatically recognized and extracted features can effectively improve the control performance on the intelligent lower limb prosthesis and realize the natural and seamless conversion of the intelligent prosthesis in a variety of motion modes.

Activity, socket fit, comfort, and community participation in lower limb prosthesis-users: A Cambodian cohort study

Background: After amputation, many people become less active, feel lonely and lose independence. Understanding the factors associated with low physical activity levels and participation could contribute to defining key interventions which can support prosthesis-users so they can live a more active and socially inclusive lifestyle. This longitudinal observational study aims to measure correlations between physical activity, community participation, prosthetic fit, comfort, and user satisfaction using actimetry, 3D scans and questionnaires in a Cambodian cohort of established lower limb prosthesis-users.Methods: Twenty participants completed a questionnaire which included their demographics, community participation, prosthesis satisfaction and comfort at the start of the study. A repeat assessment was included between 3 and 6 months later. Their prosthetic sockets and residual limbs were 3D scanned at the start and end of the study. Accelerometers were embedded under the cosmesis on the shank of the prosthesis, to collect 10 weeks of activity data.Results: Participants averaged 4470 steps/day (743-7315 steps/day), and wore their prosthesis for most waking hours, averaging 13.4 hours/day (4.5-17.6 hours/day). Self-reported measures of activity and hours of wear correlated with these accelerometer data (Spearman’s rho rs = 0.59, and rs = 0.71, respectively). Participants who were more active wore their prosthesis for more hours/day (Pearson r = 0.73) and were more satisfied with socket fit (rs = 0.49). A longer residual limb correlated with better community participation (rs = 0.56) and comfort (rs = 0.56). Self-reported community participation did not correlate with a person’s activity level (rs = 0.13), or their prosthesis comfort (rs = 0.19), and there was only weak correlation between how important the activity was to an individual, and how often they participated in it (rs = 0.37). A simple 0-10 scale of comfort did not provide enough detail to understand the types and severity of discomfort experienced.Conclusion: Associations between perceived and measured activity levels correlated with socket satisfaction in the cohort of people with established lower limb amputations. The small sample size means these correlations should be used with caution, but they indicate variables worthy of further study to understand barriers to community engagement and physical activity for prosthesis-users in Cambodia.

Group-based telerehabilitation intervention using Wii Fit to improve walking in older adults with lower limb amputation (WiiNWalk): A randomized control trial

Objective Determine efficacy of the novel WiiNWalk intervention on walking-related outcomes in older adults with lower limb amputation. Design Multi-site, parallel, evaluator-masked randomized controlled trial. Setting Home-setting in three Canadian cities. Participants Community-dwelling lower limb prosthesis users over 50 years of age. Interventions The WiiNWalk group (n = 38) used modified Wii Fit activities for prosthetic rehabilitation. The attention control group (n = 33) used Big Brain Academy: Wii Degree, comprising of cognitive activities. Both groups completed a 4-week supervised phase with three 1-h sessions/week in groups of three overseen by a clinician via videoconferencing and a 4-week unstructured and unsupervised phase. Main Measures Primary outcome was walking capacity (2 min walk test); secondary outcomes were balance confidence (activities-specific balance confidence scale), dynamic balance (four-step square test), and lower limb functioning (short physical performance battery). Outcomes were compared across time points with repeated measures analysis of covariance, adjusting for baseline and age. Results Mean age was 65.0 (8.4) years, with 179.5 (223.5) months post-amputation and 80% transtibial amputation. No group difference in a 2 min walk test with an effect size: 1.53 95% CI [−3.17, 6.23] m. Activities balance confidence was greater in the WiiNWalk group by 5.53 [2.53, 8.52]%. No group difference in the four-step square test −0.16 [−1.25, 0.92] s, nor short physical performance battery 0.48 [−0.65, 1.61]. A post-hoc analysis showed the greatest difference in balance confidence immediately after an unsupervised phase. Conclusions The WiiNWalk intervention improved balance confidence, but not walking-related physical function in older adult lower limb prosthesis users. Future rehabilitation games should be specific to the amputation context. Clinical Trial Registration number, NCT 01942798.

Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One-Dimensional Dual-Tree Complex Wavelet Transforms

The motion intent recognition via lower limb prosthesis can be regarded as a kind of short-term action recognition, where the major issue is to explore the gait instantaneous conversion (known as transitional pattern) between each two adjacent different steady states of gait mode. Traditional intent recognition methods usually employ a set of statistical features to classify the transitional patterns. However, the statistical features of the short-term signals via the instantaneous conversion are empirically unstable, which may degrade the classification accuracy. Bearing this in mind, we introduce the one-dimensional dual-tree complex wavelet transform (1D-DTCWT) to address the motion intent recognition via lower limb prosthesis. On the one hand, the local analysis ability of the wavelet transform can amplify the instantaneous variation characteristics of gait information, making the extracted features of instantaneous pattern between two adjacent different steady states more stable. On the other hand, the translation invariance and direction selectivity of 1D-DTCWT can help to explore the continuous features of patterns, which better reflects the inherent continuity of human lower limb movements. In the experiments, we have recruited ten able-bodied subjects and one amputee subject and collected data by performing five steady states and eight transitional states. The experimental results show that the recognition accuracy of the able-bodied subjects has reached 98.91%, 98.92%, and 97.27% for the steady states, transitional states, and total motion states, respectively. Furthermore, the accuracy of the amputee has reached 100%, 91.16%, and 90.27% for the steady states, transitional states, and total motion states, respectively. The above evidence finally indicates that the proposed method can better explore the gait instantaneous conversion (better expressed as motion intent) between each two adjacent different steady states compared with the state-of-the-art.

A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees

There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users.