Background: Scanners are becoming widespread in Prosthetics and Orthotics, replacing plaster casting in the manufacture of some types of devices. P&O shape capture must be accurate and reliable, so the device is comfortable and reproducible between clinicians/sessions.

Objectives: To map knowledge on measuring accuracy and reliability of spatial data produced from 3D scanners.

Methods: The study design was a scoping review using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Studies published in or after 2010 in English with a full-text available that analyse either the accuracy/validity or reliability of human 3D scanning data within a P&O context. Sources were obtained from Pubmed, CINAHL, Scopus, Cochrane Library, Web of Science, and AMed databases on 25th March.

Results: The search identified 115 studies of which 9 were included (7 experimental [4 prosthetic, 3 orthotic], 1 systematic review, 1 literature review). 7 analysed both reliability and accuracy/validity and 2 analysed reliability. High heterogeneity amongst studies’ methods, techniques, and equipment.

Conclusion: Methods, techniques, and equipment used to measure accuracy/validity and reliability varied greatly though more so in the measurement of accuracy/validity. Within the studies, researchers called for more research on standardisation of measurement methods and techniques.

Background and Aim: Slip events are responsible for up to 20% of falls and often result in severe injuries, and significant mortality and morbidity. Prior research has revealed several factors that increase the likelihood of a slip event including walking with a large foot-floor contact angle (FFCA) at heel-strike (>20°). Numerous feedback systems leveraging wearable sensors that measure gait-related kinematic or kinetic data have been used to improve balance and gait performance. In this feasibility study we demonstrated the use of a wearable feedback device for modifying FFCA during treadmill walking.

Methods: Ten healthy participants (3 females and 7 males, aged 22.0±1.6 years) with fewer than 75% of baseline overground FFCA values falling within a range of 10-20° were recruited for inclusion in the study. A feedback system comprising two IMUs attached to the mid-foot regions of participants' dominant and non-dominant feet to measure FFCA during heel strike events, a laptop for calculating FFCA, and speakers for providing auditory cues to participants was used. Participants received cues during the non-dominant foot stance phase if the average of the two preceding dominant FFCAs was outside of the target range (10-20°). Participants performed 2-min baseline and post-training treadmill trials with a speed of 1.35 m/s prior to and following four 4-min treadmill training trials with FFCA feedback. The percentage of FFCAs within the target range, and the mean and variability of FFCAs were computed for baseline, training, and post-training trials, and one-way repeated measures ANOVA and post-hoc comparisons were performed. The significance level was 0.05.

Results: Participants increased their percentage of FFCAs within the target range when feedback was provided during the training trials compared to the no feedback condition during the baseline trials (66.9% vs. 53.9%, P=0.028). Increased percentages of FFCAs within the target range were also observed during the post-training trials (75.8% vs. 53.9%, P=0.027). The average FFCA increased from 9.9° during baseline trials to 13.7° during training trials (P=0.028). The FFCAs were less variable during the training (P=0.028) and post-training (P=0.028) trials compared to the baseline trials.

Conclusions: The findings suggest that participants could use the auditory cues to dynamically adjust their FFCAs while walking on a treadmill and that the training effects were present for a short period of time following the completion of the training. The FFCA is one of several gait parameters that could be used for gait training purposes to potentially reduce the likelihood of a slip event. Future work should examine the effects of gait training with FFCA feedback on the incidence and severity of slips, and on other gait parameters.

BACKGROUND 

Applying wearable motion sensors to capture balance/gait performance and provide the corresponding biofeedback/reminder have been proved effective in improving users’ balance/gait [1-5]. Unfortunately, previous approach of providing pre-set biofeedback did not consider user’s individual balance performance or training process during various tasks. Big data analytics and machine learning technologies have been widely used to monitor the daily physical activity [6-8]. However, few previous studies have utilized these technologies to improve balance/gait training.

AIM

This study aimed to develop a foot-motion based smart insole and smartwatch system integrated with big data analytics, and investigate its effect on improvement of balance training in patients with stroke.

METHOD 

The newly-developed system with big data analytics can collect and store patients’ balance performance and their response to the reminder/biofeedback during each session of balance/gait training. With the collected huge amount of data (big data) of patients’ balance and response to the biofeedback, the system can identify and extract the feature of patients’ response upon receiving the biofeedback, and further deliver the customized biofeedback (that gradually changed according to the balance improvement) for patients to further improve balance and gait training outcomes (machine learning).

A randomized controlled trial will be conducted on 12 patients with stroke by evaluating patient’s balance/gait training outcomes with and without using the developed system.

RESULTS

The development of hardware of the system were completed, and the development of software were in progress. The system contained: 1) personal unit with force and motion sensors placed at both feet to capture the foot motion, and a smartwatch at wrist to collect data from both feet via Bluetooth and then transmit the data to the central cloud server via WiFi; 2) central cloud servers for data transmission and storage; 3) user interface for data analysis, which included a smartphone, tablet, and/or laptop; and 4) workstation for big data analytics (Figure 1). The collected data involved all sensor signals the system received before and after delivering biofeedback, and from day to day monitoring of patients. The customized biofeedback pattern included various type, frequency, magnitude, and amount/dosage of biofeedback.

DISCUSSION AND CONCLUSION 

The introduced system adopted big data and machine learning technologies to provide the repetitive targeted balance and gait training based on each patient’s condition. With further optimization, this system can also be applied in elderly and other patients with balance disorders for various daily tasks, including standing, walking, and obstacle crossing. This will enhance the balance training outcomes and potentially reduce the risk of falls in the future.

REFERENCES

1. Ma, C.Z.-H.; 2018 Top Stroke Rehabil.
2. Ma, C.Z.-H.; 2017 Hum Mov Sci.
3. Ma, C.Z.-H.; 2016 Sensors.
4. Ma, C.Z.-H.; 2015 Sensors.
5. Wan, A.-H.; 2016 Arch Phys Med Rehabil.
6. Wu, J.; 2017 INT J PROD RES.
7. Badawi, H.F.; 2017 Future Gener Comput Syst.
8. Gravina, R.; 2017 Future Gener Comput Syst.

ACKNOWLEDGEMENTS

This work was partially supported by The Hong Kong Polytechnic University [grant number: G-YBRN].

A portable and lightweight vibrotactile biofeedback system is introduced here which acts as a real-time balance aid. The biofeedback system provided users with vibrotactile stimulation based on changes in plantar pressure distribution. An experiment was conducted to investigate its effectiveness in improvement of postural control. A Romberg test was performed requesting the subjects to stand as still as possible, while the degree of body sway was measured by a force platform. Two young healthy subjects and one older healthy subject participated in the study. A wearing wool socks and eye-closed intervention was used to simulate reduced sensory input, and the effect of provision of vibrotactile feedback was studied. The experiment was conducted in 3 conditions: 1) bare feet, eyes open (baseline), 2) wearing 5 layers of wool socks, eyes closed, 3) wearing 5 layers of socks, eyes closed, with biofeedback system turned-on. The range, root mean square (RMS) and coefficient of variance (CV) of center of pressure (COP) were studied. Results indicated a significant increase of postural sway after the intervention of reducing sensory inputs, and a considerable reduction of postural sway upon using the vibrotactile feedback reminding body motion in four directions. These results suggested that vibrotactile biofeedback system is effective in improving postural control of subjects. Future studies about the effects of this biofeedback system on dynamic balance control and gait are needed.

AIMS: The objective of this study was to test the accuracy and diagnostic interchangeability of tissue Doppler-based displacement, velocity, strain, and strain rate measurements in commercially used ultrasound (US) systems. METHODS AND RESULTS: Using an in-house made phantom, four different US scanner models were evaluated. Two different scanners of the same model were tested, and one scanner acquisition was tested twice with two generations of the same workstation giving six test results in total. The scanners were in active clinical use and are subject to regular maintenance checks. There were three displacement and four velocity results that stood out from the rest and could be regarded as accurate and interchangeable. Among the deformation measurements, three acceptable strain results were found while there were no acceptable strain rate results. Furthermore, the study showed that measurements from scanners of the same model, same acquisition post-processed on different workstations and repeated measurements from the same scanner, can yield disparate results. CONCLUSION: Measurements that are accurate and of interchangeable use can be found for displacement and velocity measurements, but are less likely to be found for strain and strain rate measurements. It is strongly recommended that the ability of each individual US scanner to measure displacement, velocity, strain, and strain rate is evaluated before it is introduced into clinical practice, and it must always be evaluated together with the workstation the scanner is intended to be used in conjunction with.