Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.

We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system

Bradycardia Caused by interaction of Venlafaxine and Cyclosporine: A case report

Background: Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are prescribed widely for the treatment of depression, anxiety disorders and other psychiatric disorders. Although antidepressants are considered as a safety drug category but unexpected cardiovascular events have been reported as the most serious complications. The aim of this study was to introduce a case presentation on bradycardia due to the drug interference of venlafaxine and cyclosporine. Case presentation: The patient was a 38-year old woman diagnosed with systemic lupus erythematosus 5 years ago, who was admitted to a general educational hospital in northern Iran due to intensified rheumatologic symptoms and complaining about abdominal pain. Cyclosporine tab were administered to the patient, 50 mg twice daily. Two weeks after the administration of cyclosporine, the level of blood cyclosporine was checked. The patient became bradycardic after starting a single dose of venlafaxine (heart rate 52 ppm). Cardiac assessment showed no reason for bradycardia and it subsided after a drop of venlafaxine. Conclusion: As a result of the potential adverse drug interactions between cyclosporine and antidepressants such as venlafaxine, physicians should be aware of the possibility of bradycardia in the simultaneous prescription of these drugs in cases

Human chorionic gonadotropin attenuates amyloid-β plaques induced by streptozotocin in the rat brain by affecting cytochrome c-ir neuron density

Objective(s): Amyloid β plaques, in Alzheimer’s disease, are deposits in different areas of the brain such as prefrontal cortex, molecular layer of the cerebellum, and the hippocampal formation. Amyloid β aggregates lead to the release of cytochrome c and finally neuronal cell death in brain tissue. hCG has critical roles in brain development, neuron differentiation, and function. Therefore, we investigated the effect of hCG on the density of the congophilic Aβ plaque and cytochrome c-ir neurons in the hippocampus, prefrontal cortex, and cerebellum of Streptozotocin (STZ)-treated rats. Materials and Methods: Alzheimer model in rats (except the control group) was induced by streptozotocin (3 mg/kg, Intracerebroventricularly (ICV)). Experimental group rats received streptozotocin and then different doses of hCG (50, 100, and 200 IU, intraperitoneally) for 3 days. 48 hr after last drug injection and after histological processing, the brain sections were stained by congo red for congophilic amyloid β plaques and cytochrome c in the hippocampus, prefrontal cortex, and cerebellum were immunohistochemically stained. Results: Density of congophilic Aβ plaques and cytochrome c-immunoreactive neurons was significantly higher in ICV STZ treated rats than controls. Treatment with three doses of hCG significantly decreased the density of congophilic Aβ plaques and cytochrome c-immunoreactive neurons in the rat hippocampus, prefrontal cortex, and cerebellum in ICV STZ-treated rats (

Cognitive-Behavioral Therapy and Hypnosis Intervention on Anxiety, Depression, and Quality of Life in Patients with Breast Cancer Undergoing Chemotherapy: A Clinical Trial

Background: Women with breast cancer undergo painful and distressing treatment procedures. Hypnotherapy and cognitive-behavioral therapy (CBT) could be considered as an effective therapy. Method: In this clinical trial, 50 women aged 25 to 65 were assigned to three groups (CBT, hypnosis, and control groups). Eight one-hour treatment sessions were run for each of the hypnosis and CBT groups. We utilized The European Organization for Research and Treatment of Breast Cancer-specific Quality of Life (QoL), The European Organization for Research and Treatment of Cancer QoL questionnaires, and The Hospital Anxiety and Depression Scale for the evaluation of the QoL, anxiety, and depression at the beginning and end of the treatment, as well as six months post-treatment. Results: The improvements in the stress, depression, and qoL amongst the three groups were significant, although these improvements in CBT group were more than those in hypnosis group, and in hypnosis and CBT groups were not significant. Physical functioning, body image, sexual functioning, arm symptoms, breast symptoms, future perspective, pain, digestive problems, and functional scale significantly changed in CBT and hypnosis groups (p <0.05). Memory and social functioning; however, did not change in the groups and across the three groups. In addition, sleeping disorders and emotional malfunctioning were recovered only in the hypnosis group, which was statistically significant. Conclusion: We found hypnosis exclusively effective on reducing certain problems of breast cancer patients, such as sleeping disorders and emotional malfunctioning; therefore, it is suggested as an efficient solution for these patients’ problems

Pedestrian Dead Reckoning Using Smartphone Inertial Data for Blind Wayfinding

Accurate, robust, and infrastructure-free pedestrian positioning and navigation systems have gained significant attention in recent years due to their diverse applications. GPS is ineffective indoors and fixed infrastructure-based indoor navigation systems, such as beacons or Wi-Fi networks, pose practical and cost challenges. To address this, there’s a growing demand for self-reliant navigation systems that seamlessly function indoors and outdoors. These systems often utilize sensor fusion and machine learning for precise and adaptive navigation. They can be integrated into standard smartphones, providing a portable and comprehensive navigation tool.A specific beneficiary group for such systems includes individuals with visual impairments who rely on tools such as long canes or guide dogs. Self-reliant navigation systems can be customized for their specific needs, enhancing mobility and independence indoors and outdoors. Existing research has primarily focused on sighted individuals, however, there is an increasing interest to understand the unique challenges faced by visually impaired individuals and optimizing systems for more inclusive and effective solutions. This dissertation addresses this need by developing a Pedestrian Dead Reckoning (PDR) system for inertial navigation using smartphones to assist visually impaired individuals in indoor settings. Such PDR system requires two important components: step detection and step length estimation. For step detection within our system, an LSTM-based network was developed, trained, and tested on the WeAllWalk data set, which includes inertial data gathered from ten blind and five sighted walkers. The achieved results on this data set surpassed existing benchmarks, highlighting the crucial role of selecting from the walker community for training data plays in determining results. Furthermore, the PDR system, incorporating this step detector method, outperformed the state-of-the-art learning-based model, RoNIN, in path reconstruction on the WeAllWalk data set. For step length estimation, a model consisting of an LSTM layer followed by four fully connected layers was implemented. The same network scheme was used to predict either step length or walking speed (allowing for integration over a step period to calculate step length). In the initial step, data was collected from twelve sighted participants who traversed four routes with varying stride lengths. Results from sighted participants suggest that step length can be predicted more reliably than average walking speed over each step. Subsequently, the model was trained and tested on data from seven blind participants. The obtained results highlighted the different gait patterns among sighted and blind walkers, emphasizing the importance of designing systems for assisted navigation based on data from the visually impaired community. Finally, an iOS application named WayFinding was designed to aid indoor navigation for blind travelers. The developed step detector module was integrated into this app. However, for this study, a calibrated step length was used instead of the step length estimator. WayFinding enables an individual to determine and follow a route through building corridors to reach a certain destination, assuming the app has access to the building’s floor plan. This app exclusively utilizes the inertial sensors of the smartphone, requiring no infrastructure modifications, such as the installation and support of BLE beacons. A watch-based user interface and speech-based notifications enable hands-free interaction for blind users. A user study involving seven blind participants was conducted in our campus buildings to assess the system’s performance. All participants successfully navigated the pre-defined routes and provided positive feedback during the post-experiment interviews and questionnaires

Step Length Estimation for Blind Walkers

Wayfinding systems using inertial data recorded from a smartphone carried by the walker have great potential for increasing mobility independence of blind pedestrians. Pedestrian dead-reckoning (PDR) algorithms for localization require estimation of the step length of the walker. Prior work has shown that step length can be reliably predicted by processing the inertial data recorded by the smartphone with a simple machine learning algorithm. However, this prior work only considered sighted walkers, whose gait may be different from that of blind walkers using a long cane or a dog guide. In this work, we show that a step length estimation network trained on data from sighted walkers performs poorly when tested on blind walkers, and that retraining with data from blind walkers can dramatically increase the accuracy of step length prediction

Step Length Is a More Reliable Measurement Than Walking Speed for Pedestrian Dead-Reckoning*

Pedestrian dead reckoning (PDR) relies on the estimation of the length of each step taken by the walker in a path from inertial data (e.g. as recorded by a smartphone). Existing algorithms either estimate step lengths directly, or predict walking speed, which can then be integrated over a step period to obtain step length. We present an analysis, using a common architecture formed by an LSTM followed by four fully connected layers, of the quality of reconstruction when predicting step length vs. walking speed. Our experiments, conducted on a data set collected by twelve participants, strongly suggest that step length can be predicted more reliably than average walking speed over each step

Smartphone-Based Inertial Odometry for Blind Walkers.

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers

Quetiapine‐induced chest wall edema with the swellings of face and extremities in a young hospitalized patient: A case report

Key Clinical Message Quetiapine can lead to the face, extremity and particularly chest wall edema in hospitalized patients in the supine position. Abstract Quetiapine (QTP) is known as an atypical antipsychotic agent with some adverse effects, such as edema. However, along this line, peripheral edema is not a life‐threatening episode, but it is an important side effect affecting medical compliance. Therefore, QTP‐induced chest wall edema with the swellings of the face and the extremities is very rare. This report is about a young man who was admitted in the intensive care unit with multiple trauma (MT). On account of his delirious state, QTP was started at 25 mg and then increased to 75 mg, three times a day. The patient developed swelling of the face, the upper and lower limbs, and the chest wall. After stopping the QTP use, his edema went down. Although there is still speculation about the possible mechanisms of antipsychotic‐induced edema, some studies have pointed to the relationship between dopaminergic antagonism and peripheral edema. Therefore, it is very important to pay close attention to this side effect