Effects of motor and cognitive complex training on obstacle walking and brain activity in people with Parkinson's disease: a randomized controlled trial.

BACKGROUND: The difficulties in obstacle walking are significant in people with Parkinson's disease (PD) leading to an increased fall risk. Effective interventions to improve obstacle walking with possible training-related neuroplasticity changes are needed. We developed two different exercise programs, complex walking training and motor-cognitive training, both challenging motor and cognitive function for people with PD to improve obstacle walking. AIM: To investigate the effects of these two novel training programs on obstacle walking and brain activities in PD. DESIGN: A single-center randomized, single-blind controlled study. SETTING: University laboratory; outpatient. POPULATION: Individuals with idiopathic PD. METHODS: Thirty-two participants were randomly assigned to the complex walking training group (N.=11), motor-cognitive training group (N.=11) or control group (N.=10). Participants in training groups received exercises for 40 minutes/session, with a total of 12-session over 6 weeks. Control group did not receive additional training. Primary outcomes included obstacle walking, and brain activities (prefrontal cortex (PFC), premotor cortex (PMC), and supplementary motor area (SMA)) during obstacle walking by using functional near-infrared spectroscopy. Secondary outcomes included obstacle crossing, timed up and go test (TUG), cognitive function in different domains, and fall efficacy scale (FES-I). RESULTS: The motor-cognitive training group demonstrated greater improvements in obstacle walking speed and stride length, SMA activity, obstacle crossing velocity and stride length, digit span test, and TUG than the control group. The complex walking training did not show significant improvement in obstacle walking or change in brain activation compared with control group. However, the complex walking training resulted in greater improvements in Rey-Osterrieth Complex Figure test, TUG and FES-I compared with the control group. CONCLUSIONS: Our 12-session of the cognitive-motor training improved obstacle walking performance with increased SMA activities in people with PD. However, the complex walking training did not lead such beneficial effects as the cognitive-motor training. CLINICAL REHABILITATION IMPACT: The cognitive-motor training is suggested as an effective rehabilitation program to improve obstacle walking ability in individuals with PD.

Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment.

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge-desire-intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users' beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.