Swarm Intelligence in Internet of Medical Things: A Review.

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

Bringing objectivity to motor skill assessment in children.

The study purpose was to use Inertial Measurement Units (IMUs) to objectively assess children's motor competence in seven movement skills. Fourteen children aged from seven to 12 years (M = 9.64) participated. Children were asked to perform up to 10 trials of each skill. Children performed the skills, which were captured by XSENS MVN Awinda wireless motion capture, and video. Skills were assessed from video as per the criteria from the Test of Gross Motor Development 3. Initially, 17 IMU sensors were used for signal processing, but this was restricted to four sensors (wrists and ankles) to be more feasible for field assessment. Results of the signal testing against its modelled "Good" signal, showed the skip was classified correctly each time, as was the sidestep. Accuracy % rates for each skill were: kick (95.2), catch (95.0), throw (80.5), jump (78.9), and hop (76.9). Using signal processing-based methods via four sensors was a reliable and feasible way to assess seven motor skills in children. This approach means monitoring and assessment of children's skills can be objective, which will potentially reduce the time involved in motor skill assessment and analysis for research, clinical, sport and education purposes.

Nutritional and Behavioral Countermeasures as Medication Approaches to Relieve Motion Sickness: A Comprehensive Review.

The mismatch in signals perceived by the vestibular and visual systems to the brain, also referred to as motion sickness syndrome, has been diagnosed as a challenging condition with no clear mechanism. Motion sickness causes undesirable symptoms during travel and in virtual environments that affect people negatively. Treatments are directed toward reducing conflicting sensory inputs, accelerating the process of adaptation, and controlling nausea and vomiting. The long-term use of current medications is often hindered by their various side effects. Hence, this review aims to identify non-pharmacological strategies that can be employed to reduce or prevent motion sickness in both real and virtual environments. Research suggests that activation of the parasympathetic nervous system using pleasant music and diaphragmatic breathing can help alleviate symptoms of motion sickness. Certain micronutrients such as hesperidin, menthol, vitamin C, and gingerol were shown to have a positive impact on alleviating motion sickness. However, the effects of macronutrients are more complex and can be influenced by factors such as the food matrix and composition. Herbal dietary formulations such as Tianxian and Tamzin were shown to be as effective as medications. Therefore, nutritional interventions along with behavioral countermeasures could be considered as inexpensive and simple approaches to mitigate motion sickness. Finally, we discussed possible mechanisms underlying these interventions, the most significant limitations, research gaps, and future research directions for motion sickness.

Application of artificial intelligence in wearable devices: Opportunities and challenges.

BACKGROUND AND OBJECTIVES: Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data. METHODS: Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives. RESULTS: Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays. CONCLUSION: The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.

Accuracy vs. Practicality of Inertial Measurement Unit Sensors to Evaluate Motor Competence in Children.

The TGMD (i.e., Test of Gross Motor Development) has been considered as one of the gold standards of assessment tools for analysis of motor competence in children. However, it is rarely used by teachers in schools because the time, resources, and expertise required for one teacher to assess a class of students is prohibitive in most cases. A potential solution may be to automate the testing protocol using objective measures and inertial measurement unit sensors. An accurate method using 17 sensors to capture full body motion profiles and machine learning methods to objectively assess proficiency has been developed; however, feasibility of this method was low. Subsequently, a simplified method using four sensors (i.e., attached to wrists and ankles) was found to be effective, efficient, and potentially highly feasible for use in school settings. For some skills, however, not all skill criteria could be assessed. Additionally, misclassification on occasion, marred results. In the present paper we consider a previous experiment that used wireless motion capture to assess criteria from the TGMD-3. We discuss the advantages alongside the disadvantages of testing motor competence in children using sensors and consider the question-Can a compromise be struck between accuracy and feasibility?

An Uncertainty-Aware Transfer Learning-Based Framework for COVID-19 Diagnosis.

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries, and also, there are genuine concerns about their reliability and performance. Motivated by these shortcomings, this article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images. Four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, are first applied to extract deep features from chest X-ray and computed tomography (CT) images. Extracted features are then processed by different machine learning and statistical modeling techniques to identify COVID-19 cases. We also calculate and report the epistemic uncertainty of classification results to identify regions where the trained models are not confident about their decisions (out of distribution problem). Comprehensive simulation results for X-ray and CT image data sets indicate that linear support vector machine and neural network models achieve the best results as measured by accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). Also, it is found that predictive uncertainty estimates are much higher for CT images compared to X-ray images.

Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities.

BACKGROUND: Optical measurement techniques and recent advances in wearable technology have made heart rate (HR) sensing simpler and more affordable. OBJECTIVES: The Polar OH1 is an arm worn optical heart rate monitor. The objectives of this study are two-fold; 1) to validate the OH1 optical HR sensor with the gold standard of HR measurement, electrocardiography (ECG), over a range of moderate to high intensity physical activities, 2) to validate wearing the OH1 at the temple as an alternative location to its recommended wearing location around the forearm and upper arm. METHODS: Twenty-four individuals participated in a physical exercise protocol, by walking on a treadmill and riding a stationary spin bike at different speeds while the criterion measure, ECG and Polar OH1 HR were recorded simultaneously at three different body locations; forearm, upper arm and the temple. Time synchronised HR data points were compared using Bland-Altman analyses and intraclass correlation. RESULTS: The intraclass correlation between the ECG and Polar OH1, for the aggregated data, was 0.99 and the estimated mean bias ranged 0.27-0.33 bpm for the sensor locations. The three sensors exhibited a 95% limit of agreement (LoA: forearm 5.22, -4.68 bpm; upper arm 5.15, -4.49; temple 5.22, -4.66). The mean of the ECG HR for the aggregated data was 112.15 ± 24.52 bpm. The intraclass correlation of HR values below and above this mean were 0.98 and 0.99 respectively. The reported mean bias ranged 0.38-0.47 bpm (95% LoA: forearm 6.14, -5.38 bpm; upper arm 6.07, -5.13 bpm; temple 6.09, -5.31 bpm), and 0.15-0.16 bpm (95% LoA: forearm 3.99, -3.69 bpm; upper arm 3.90, -3.58 bpm; temple 4.06, -3.76 bpm) respectively. During different exercise intensities, the intraclass correlation ranged 0.95-0.99 for the three sensor locations. During the entire protocol, the estimated mean bias was in the range -0.15-0.55 bpm, 0.01-0.53 bpm and -0.37-0.48 bpm, for the forearm, upper arm and temple locations respectively. The corresponding upper limits of 95% LoA were 3.22-7.03 bpm, 3.25-6.82 bpm and 3.18-7.04 bpm while the lower limits of 95% LoA were -6.36-(-2.35) bpm, -6.46-(-2.30) bpm and -7.42-(-2.41) bpm. CONCLUSION: Polar OH1 demonstrates high level of agreement with the criterion measure ECG HR, thus can be used as a valid measure of HR in lab and field settings during moderate and high intensity physical activities.

RGB-D ergonomic assessment system of adopted working postures.

Ensuring a healthier working environment is of utmost importance for companies and global health organizations. In manufacturing plants, the ergonomic assessment of adopted working postures is indispensable to avoid risk factors of work-related musculoskeletal disorders. This process receives high research interest and requires extracting plausible postural information as a preliminary step. This paper presents a semi-automated end-to-end ergonomic assessment system of adopted working postures. The proposed system analyzes the human posture holistically, does not rely on any attached markers, uses low cost depth technologies and leverages the state-of-the-art deep learning techniques. In particular, we train a deep convolutional neural network to analyze the articulated posture and predict body joint angles from a single depth image. The proposed method relies on learning from synthetic training images to allow simulating several physical tasks, different body shapes and rendering parameters and obtaining a highly generalizable model. The corresponding ground truth joint angles have been generated using a novel inverse kinematics modeling stage. We validated the proposed system in real environments and achieved a joint angle mean absolute error (MAE) of 3.19±1.57∘ and a rapid upper limb assessment (RULA) grand score prediction accuracy of 89% with Kappa index of 0.71 which means substantial agreement with reference scores. This work facilities evaluating several ergonomic assessment metrics as it provides direct access to necessary postural information overcoming the need for computationally expensive post-processing operations.