Dimitrios-Sokratis Komaris

Wearable technology has undergone explosive growth over recent years, driven by sweeping advances in information and communications technology and shaped by fundamental shifts in demography, lifestyle, and the broader environment

Introduction: This study analyzed muscle synergies in subacute stroke patients’ gait, comparing paretic and non-paretic limbs with healthy individuals, and explored the structure and temporal activation of synergistic muscle activation patterns.Methods: Muscle synergies were computed from EMG data of 12 muscles in 138 able-bodied adults and 50 stroke survivors using non-negative matrix factorization, analyzing 350 control strides, 319 paretic strides, and 337 non-paretic strides. Temporal activation coefficients of the muscle synergies between groups of strides were also compared using cross-correlation analysis.Results: The extracted muscle synergies were consistent in composition across all groups of control and stroke subjects, with four synergies identified in gait cycles on average. The comparison of the synergies’ temporal activation returned indexes (r) ranging from 0.60 to 0.74, with differences existing in the duration and timing of the activations of the hip flexors and knee extensors, dorsal flexors, and plantar flexors.Discussion: Our findings suggest a certain degree of preserved motor function in stroke patients’ gait, even in the presence of recent hemiparesis, but with evident alterations in the synergies’ temporal activation. Stroke rehabilitation by targeting abnormal muscle synergy activations may help shape personalized treatment plans.

Non-invasive assessment of joint status using acoustic emissions (AE) is a growing research area that has the potential to translate into clinical practice. The purpose of this study is to investigate the correlation of the knee’s AE with measures of proprioception, self-assessment, and performance, as it can be hypothesised that, AE parameters will correlate with joint function metrics due to AE being recorded during interaction of the articular surfaces. Threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant. Knee AE were recorded during cycling with 30 and 60 rpm cadences using two sensors in different frequency ranges and three modes of AE event detection. Weak (0.297, p = 0.048) to moderate (0.475, p = 0.001) Spearman’s correlations were observed between longer 5STS time and AE parameters (i.e. higher median absolute energy, signal strength, longer AE event rise time and duration). Similarly, AE parameters shown correlation with lower KOOS, especially in the “Function in Sports and Recreation” and “Activities of Daily Living” subscales with correlation coefficients for higher median amplitude up to 0.441, p = 0.001 and 0.403, p = 0.004, respectively. The correlation with the TTDPM was not detected for most of the AE parameters. Additionally, a lower frequency sensor and 60 rpm cadence AE recordings showed higher correlations. Considering that this study included subjects from the general population and the number of participants with KOOS <70 was relatively small, higher correlations might be expected for clinically confirmed OA cases. Additionally, different ICCs might be expected for alternative signal parameters and proprioception assessment methods. Overall, the study confirms that AE monitoring offers an additional modality of joint assessment that reflects interaction between cartilage surfaces and can complement orthopaedic diagnostics, especially in the context of remote monitoring, drug testing, and rehabilitation.

Pressure insoles allow for the collection of real time pressure data inside and outside a laboratory setting as they are non-intrusive and can be simply integrated into industrial environments for occupational health and safety monitoring purposes. Activity detection is important for the safety and wellbeing of workers, and the present study aims to employ pressure insoles to detect the type of industry-related task an individual is performing by using random forest, an artificial intelligence-based classification technique. Twenty subjects wore loadsol® pressure insoles and performed five specific tasks associated with a typical workflow: standing, walking, pick and place, assembly, and manual handling. For each activity, statistical and morphological features were extracted to create a training dataset. The classifier performed with an accuracy over 82%, using ten-fold cross-validation, for a time window of 5 seconds, showing the potential for task classification in edge-AI applications in smart manufacturing environments. A re-analysis focused on the five most influential features obtained 83% accuracy. The combination of random forest and in-depth feature analysis (SHAP) provided insights into the importance of features and the impact of their value on each task class. Such an understanding can aid in reducing misclassifications for health and safety purposes and can aid in the design of pressure insoles that are optimized for impactful features. The accuracy achieved is comparable to similar task classification studies but with the benefit of added explainability, which increases transparency and, thereby, trust in the classifier decisions.

Objective: Knee osteoarthritis is currently one of the top causes of disability in older population, a rate that will only increase in the future due to an aging population and the prevalence of obesity. However, objective assessment of treatment outcomes and remote evaluation are still in need of further development. Acoustic emission (AE) monitoring in knee diagnostics has been successfully adopted in the past; however, a wide discrepancy among the adopted AE techniques and analyses exists. This pilot study determined the most suitable metrics to differentiate progressive cartilage damage and the optimal frequency range and placement of AE sensors. Methods: Knee AEs were recorded in the 100-450 kHz and 15-200kH frequency ranges from a cadaver specimen in knee flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. Results: AE events in the lower frequency range and the following parameters provided better distinction between intact and damaged knee: hit amplitude, signal strength, and absolute energy. The medial condyle area of the knee was less prone to artefacts and unsystematic noise. Multiple reopenings of the knee compartment in the process of introducing the damage negatively affected the quality of the measurements. Conclusion: Results may improve AE recording techniques in future cadaveric and clinical studies. Significance: This was the first study to evaluate progressive cartilage damage using AEs in a cadaver specimen. The findings of this study encourage further investigation of joint AE monitoring techniques.

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the technology is necessary to make it appropriate for use as a clinical tool. This paper presents a test-retest and intrasession reliability evaluation of AE measurements of the knee during physical tasks: cycling, knee lifts and single-leg squats. Three sessions, each involving eight healthy volunteers were conducted. For the cycling activity, ICCs ranged from 0.538 to 0.901, while the knee lifts and single-leg squats showed poor reliability (ICC < 0.5). Intrasession ICCs ranged from 0.903 to 0.984 for cycling and from 0.600 to 0.901 for the other tasks. The results of this study show that movement consistency across multiple recordings and minimizing the influence of motion artifacts are essential for higher test reliability. It was shown that motion artifact resistant sensor mounting and the use of baseline movements to assess sensor attachment can improve the sensing reliability of AE techniques. Moreover, constrained movements, specifically cycling, show better inter- and intrasession reliability than unconstrained exercises.

Smart wearables are a promising tool for the objective and quantifiable monitoring of patients’ capabilities during remote at-home assessments. A novel platform for the remote assessment of patients undergoing knee rehabilitation has been presented in this paper, SKYRE. The challenges associated with the design of the SKYRE platform are described. The platform consists of a multi-sensor wearable garment and an associated ICT architecture, with the aim of capturing real-time objective assessment of physical rehabilitation exercises and support clinicians in their decision-making process as well as provide guidance to the end-users so as to increase their awareness and compliance. The overall system architecture is defined based on users’ requirements and industrial design, and both hardware and software platforms have been thoroughly discussed in detail, including electronic design, textile integration, prototyping process, and firmware development, as well as the mobile application and web portal implementation. Multiple sensing technologies are adopted, including motion capture, electromyography measurements, and muscle electro-stimulation. The developed system, SKYRE, meets the end-users’ requirements, and the validation shows that the system presents results comparable to gold-standard technologies. SKYRE therefore might represent a valid alternative for patients and clinicians willing to perform a remote objective assessment of the rehabilitation process following knee surgery.

Background: The benefits to be obtained from home-based physical therapy programmes are dependent on the proper execution of physiotherapy exercises during unsupervised treatment. Wearable sensors and appropriate movement-related metrics may be used to determine at-home exercise performance and compliance to a physical therapy program.Methods: A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial measurement units (IMUs), after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then given wearable sensors to enable a second unsupervised data capture at home. Movement performance between the participants’ recordings was assessed with metrics of movement smoothness, intensity, consistency and control.Results: In general, subjects executed all exercises similarly when recording at home and as compared with their performance in the lab. However, participants executed all movements faster compared to the physiotherapist’s demonstrations, indicating the need of a wearable system with user feedback that will set the pace of movement.Conclusion: In light of the Covid-19 pandemic and the imperative transition towards remote consultation and tele-rehabilitation, this work aims to promote new tools and methods for the assessment of adherence to home-based physical therapy programmes. The studied IMU-derived features have shown adequate sensitivity to evaluate home-based programmes in an unsupervised manner. Cost-effective wearables, such as the one presented in this study, can support therapeutic exercises that ought to be performed with appropriate speed, intensity, smoothness and range of motion.