Studies on validation of telerehabilitation as an effective platform to help manage as well as reduce burden of care for Low-Back Pain (LBP) are sparse. This study compared the effects of Telerehabilitation-Based McKenzie Therapy (TBMT) and Clinic-Based McKenzie Therapy (CBMT) among patients with LBP. Forty-seven consenting patients with chronic LBP who demonstrated ‘directional preference’ for McKenzie Extension Protocol (MEP) completed this quasi experimental study. The participants were assigned into either the CBMT or TBMT group using block permuted randomization. Participants in the CBMT and TBMT groups received MEP involving a specific sequence of lumbosacral repeated movements in extension aimed to centralize, decrease, or abolish symptoms, thrice weekly for eight weeks. TBMT is a comparable version of CBMT performed in the home with the assistance of a mobile phone app. Outcomes were assessed at the 4th and 8th weeks of the study in terms of Pain Intensity (PI), Back Extensors Muscles’ Endurance (BEME), Activity Limitation (AL), Participation Restriction (PR), and General Health Status (GHS). Data were analyzed using descriptive and inferential statistics. Alpha level was set at p< 0.05.Within-group comparison across baseline, 4th and 8th weeks indicate that both CBMT and TBMT had significant effects on PI (p=0.001), BEME (p=0.001), AL (p=0.001), PR (p=0.001) and GHS (p=0.001) respectively. However, there were no significant differences (p>0.05) in the treatment effects between TBMT and CBMT, except for ‘vitality’ (p=0.011) scale in the GHS where TBMT led to significantly higher mean score. Mobile-app platform of the McKenzie extension protocol has comparable clinical outcomes with the traditional clinic-based McKenzie Therapy, and thus is an effective supplementary platform for care of patients with low-back pain.

In recent times, there is a paradigm shift from the use of paper based systems to the use of software systems in all spheres of life. However, the development of high quality, cost effective and useable software systems is a major challenge. One of the major obstacles confronting the successful implementation of software systems is the inability to implement all stakeholders' requirements in software development projects. This constraint is usually due to limited human resources, budget and time. Thus, most software systems have failed. It, therefore, becomes pertinent to prioritize software requirements. Requirement prioritization involves the selection of requirements that are considered more important from an accumulated list of stakeholders' requirements. There are two techniques that are used for categorizing software requirements. These techniques include the requirement prioritization methods and the negotiation methods. Requirement prioritization methods are based on different scales which include nominal scale, ordinal scale and ratio scale. The accuracy of these methods, however, is a challenge especially when prioritizing large number of requirements. Aims: Hence, this paper reviews different techniques for prioritizing requirements by highlighting their strengths and weaknesses. Techniques such as binary search tree, AHP, hierarchy AHP, priority group/Numerical Analysis, bubble sort, MoSoW, simple ranking and Planning Game were analyzed and compared in this study. Methodology: The study is based on previous literature on requirement prioritization. Results: The study showed that numerical assignment and simple ranking methods require less time in the prioritization process and they also have low scalability and reliability. The study also showed that the analytic hierarchy process requires more time for requirement prioritization; it is reliable but it is not scalable. The study also revealed that it is difficult to prioritize requirements with the existing prioritization techniques when multiple stakeholders are involved. Conclusion: The study suggests that future researches should be based on the design of requirement prioritization techniques that will have the ability to accommodate large stakeholders and requirements.  

The healthcare domain is a complex domain which lacks a unified terminological set, most especially in clinical cases. As a result of this, the messaging standards employed in the healthcare domain use different terms for the same concept which often results in clinical misinterpretation, knowledge mismanagement, misdiagnosis of the patient's illness or even death. Consequently, the healthcare system is characterized by high error rate and semantic heterogeneity. A lot of efforts have been made to resolve this problem through the use of standards, clinical terminologies, web services as well as the use of achetype. However, these solutions have proved unsuccessful in resolving semantic heterogeneity in healthcare. Ontologies have also been developed to resolve this problem by making explicit the meaning of terms used in healthcare. Ontologies provide a source of shared and precisely defined terms, resulting in interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that the healthcare domain is conceptualized results in the creation of different ontologies with contradicting or overlapping parts. Thus, the available ontologies also introduce semantic heterogeneity to this domain. An effective solution to this problem is the introduction of methods for finding matches among the various components of ontologies in healthcare in order to facilitate semantic interoperability. Therefore, this paper aims at examining the various attempts for achieving semantic interoperability in healthcare and also motivates the critical needs for ontology matching in healthcare systems.

Purpose. The study compared the influence of Clinic-based McKenzie Therapy (CbMT) and a Virtual Reality Game (VRG) version on pain intensity, back extensor muscles endurance, activity limitation, participation restriction, fear avoidance belief, kinesiophobia, and general health status of patients with chronic non-specific low-back pain.Methods. This quasi-experimental study involved 46 patients (CbMT: n = 24; VRG: n = 22) with 'directional preference' for extension, randomized into CbMT or VRG group. Treatment was applied thrice weekly for 8 weeks. Outcomes were assessed at the end of the 4 th and 8 th week. Data analysis employed descriptive and inferential statistics of independent t-test, Mann-Whitney U test, repeated measure ANOVA, Friedman's ANOVA, and ANCOVA. The significance level was set as = 0.05.Results. There were no significant differences in the treatment outcomes (mean change) across the groups (p > 0.05), except for kinesiophobia, where VRG led to a significantly higher decline in mean rank at week 4 (28.3 vs. 19.1; p = 0.018) and 8 (28.7 vs. 18.7; p = 0.009), and vitality (a general health status item) at week 4 (27.6 vs. 19.8; p = 0.042) and 8 (28.1 vs. 19.3; p = 0.042). ANCOVA showed that significant baseline parameters were not significant predictors of vitality (F = 1.986; p = 0.070) or kinesiophobia (F = 0.866; p = 0.563) outcomes. Conclusions. The VRG mode of McKenzie therapy is comparable with the clinic-based approach in most outcomes. VRG has a superior effect on kinesiophobia, but may take a higher toll on vitality/energy. Citation: Mbada CE, Makinde MO, Odole AC, Dada OO, Ayanniyi O, Salami AJ, Gambo IP. Comparative effects of clinicand virtual reality-based McKenzie extension therapy in chronic non-specific low-back pain. Hum Mov. 2019;20(3):66-79; doi: https://doi.org/10.5114/hm.2019.83998.

Advances in robotics have paved the way for a novel approach of organizing large numbers of robots, otherwise referred to as multi-robots. Multi-robots can either be homogenous or heterogeneous. Nevertheless, a group of autonomous and relatively homogenous robots that interacts with one another as well as with their environment is referred to as swarm robots. Swarm robots are biologically inspired by natural swarms as found in animal societies such as birds and fishes as well as social insects such as honey bees, wasps, termites and ants. Hence, they exhibit certain properties which are similar to those found in these creatures such as aggregation, self-organization, foraging as well as flocking. Swarm robots work together to achieve a desired goal, which is usually too complex for a single robot to accomplish. They are typically characterized by simplicity of individuals, fault tolerance, autonomy, parallelism, high reliability, scalability as well as robustness. They can be used for mining, military, medical and agricultural activities. They can also be used for search and rescue missions, toxic waste cleanup, and for piling sandbags along coastlines in preparation for floods or hurricane. Nevertheless, swarm robots are plagued with the stigma of widespread, interference, uncertainty, safety and lack of reliable communication. Furthermore, studies in swarm robotics are practically limited to virtual reality simulations. Hence, the principles of swarm robotics are rarely applied to real-life problems.  It is against this background that this study systematically explores swarm robots. This study reviewed eighty literatures relating to swarm robots. These literatures were obtained from journal articles, technical reports, books, and conference proceedings. The selection of these literatures was based on their relevance to the research problem. This study revealed that the application of swarm robots to real life problems would promote the development of systems that are robust, fault tolerant and scalable.

The foundation for any software system is its architecture. Software architecture is a view of the system that includes the system’s major components, the behaviour of those components as visible to the rest of the system, and the ways in which the components interact and coordinate to achieve the overall system’s goal. Every efficient software system arises as a result of sound architectural basement. This requires the use of good architecture engineering practices and methods. This paper recognizes software architecture practice as a discipline pervading all phases of software development and also presents an enhanced model for software engineering process which provides an avenue for speedy, efficient and timely delivery of software products to their intended users. The integration of software architecture into the phases of software development process in a generic software life cycle is also contained in this research report. This is to enable software engineers and system analysts to use effective software architecture practices and to employ appropriate methodology during the software engineering process

Brain-Computer Interface (BCI) otherwise known as a Brain-Machine Interface (BMI) is an emergent technology whose goal is to create a real-time and direct communication pathway between the brain and external devices such as computers, robots, artificial limbs and wheelchairs. In BCI, cerebral or brain activities control these devices by transmitting and receiving signals from the brain. BCI is applied in healthcare to improve the communication capabilities of people living with disabilities or locked in syndrome such as traumatic brain disorders, Amyotrophic Lateral Sclerosis (ALS), spinal cord injury, brain stem stroke and other severe motor disabilities. BCI also increases the independence of disabled individuals by improving their muscle control. Consequently, BCI improves the quality of life of disabled persons by allowing this group of people to live a normal and comfortable life. In spite of the benefits of BCI, the technology is not widely deployed in healthcare. This is because of the numerous challenges associated with it. One of the basic limitations of BCI is that the signals received from the brain are prone to interference. Furthermore, legal and ethical concerns such as the risk of infection or hemorrhage, psychological

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