Probabilistic locomotion mode recognition with wearable sensors

Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to assist humans in walking activities. We present a probabilistic Bayesian approach with a sequential analysis method for recognition of locomotion and phases of the gait cycle. Our approach uses recursive accumulation of evidence, as biological systems do, to reduce uncertainty present in the sensor measurements, and thus improving recognition accuracy. Data were collected from a wearable sensor, attached to the shank of healthy human participants, from three locomotion modes; level-ground walking, ramp ascent and ramp descent. We validated our probabilistic approach with recognition of locomotion in steady-state and gait phases in transitional states. Furthermore, we evaluated the effect, in recognition accuracy, of the accumulation of evidence controlled by increasing belief thresholds. High accuracy results achieved by our approach, demonstrate its potential for robust control of lower limb wearable soft robotic devices to provide natural and safe walking assistance to humans

Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors

Recognition of movement is a crucial process to assist humans in activities of daily living, such as walking. In this work, a high-level method for the simultaneous recognition of locomotion and gait phases using wearable sensors is presented. A Bayesian formulation is employed to iteratively accumulate evidence to reduce uncertainty, and to improve the recognition accuracy. This process uses a sequential analysis method to autonomously make decisions, whenever the recognition system perceives that there is enough evidence accumulated. We use data from three wearable sensors, attached to the thigh, shank, and foot of healthy humans. Level-ground walking, ramp ascent and descent activities are used for data collection and recognition. In addition, an approach for segmentation of the gait cycle for recognition of stance and swing phases is presented. Validation results show that the simultaneous Bayesian recognition method is capable to recognize walking activities and gait phases with mean accuracies of 99.87% and 99.20%. This process requires a mean of 25 and 13 sensor samples to make a decision for locomotion mode and gait phases, respectively. The recognition process is analyzed using different levels of confidence to show that our method is highly accurate, fast, and adaptable to specific requirements of accuracy and speed. Overall, the simultaneous Bayesian recognition method demonstrates its benefits for recognition using wearable sensors, which can be employed to provide reliable assistance to humans in their walking activities

Prediction of gait events in walking activities with a Bayesian perception system

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots

Further support for the psychometric properties of the Farsi version of Perth Alexithymia Questionnaire

Alexithymia is defined as the lack of words to describe emotions and is associated with different psychopathologies. Various tools have been developed for measuring alexithymia; each has its limitations. A new questionnaire, Perth Alexithymia Questionnaire (PAQ), was developed to simultaneously assess positive and negative dimensions. Validation of such a tool in different cultures allows cross-cultural health psychology studies and facilitates knowledge transfer in the field. We aimed to examine the psychometric features of the PAQ in the Farsi-speaking population in Iran. Four-hundred-twenty-nine university students were asked to complete the PAQ, the Toronto Alexithymia Scale (TAS-20), Beck Depression Inventory (BDI-II), Beck Anxiety Inventory (BAI), and emotion regulation questionnaire (ERQ). Concurrent validity, discriminant validity, internal consistency, and test-retest reliability and factor structure were investigated. Confirmatory factor analysis showed a five-factor model identical to the original questionnaire. The questionnaire indicated good internal consistency (0.82 &lt; α &lt; 0.94). Test-retest reliability was acceptable for all subscales. The correlations between PAQ and its subscales with BDI-II, BAI, and TAS, and expression suppression subscale of ERQ were strong for concurrent validity. Concerning the discriminant validity, PAQ and its subscales were not correlated with reappraisal subscales of ERQ. The present findings suggest that the Farsi version of PAQ has strong psychometric properties and is appropriate for use in the Farsi-speaking population.</p

The Comparative Study of the Effectiveness of Cimetidine, Ranitidine, Famotidine, and Omeprazole in Treatment of Children with Dyspepsia

Background. Functional dyspepsia is a common chronic disorder with non specific upper abdominal pain or discomfort. Different approaches with anti-secretory, spasmolytic, prokinetic and anti-inflammatory effects and most preferably reduction of visceral hypersensitivity seem logical. In this study, we compared the effectiveness of the four most drugs used for treatment of dyspepsia in children. Methods. 169 patients between 2 to 16 years old that 47.3% was male and 52.7% was female were enrolled in this clinical trial study by the diagnosis of functional dyspepsia. Then for each patient one of the drugs; Omeprazole, Famotidine, Ranitidine or Cimetidine was administered, for a period of 4 weeks. Patients were followed after 2 and 6 weeks from the beginning of the treatment. Results. The distribution of drugs between these patients were including; 21.9% with Cimetidine, 21.3% with Famotidine, 30.8% with Omeperazole and 26% with Ranitidine that the proportion of patients with all symptoms relief were: 21.6% for Cimetidine, 44.4% for Famotidine, 53.8% for Omeprazole and 43.2% for Cimetidine (P = .024). In followups within 2 and 6 weeks after beginning medical therapy, no side effects due to drugs were seen. Conclusion. If a cure is defined as all symptoms relief after a period of 4 weeks treatment, our findings showed that Omeperazole are superior to Ranitidine, Famotidine, and Cimetidine for management of functional dyspepsia

The clinical presentation of Post-Transplant Lymphoproliferative Disorder (PTLD) following pediatric liver transplantation

Post-transplant lymphoproliferative disorder is a life-threatening complication of solid organ transplantation. In pediatric recipients, PTLD has been reported in 6.4-19.5 of lung, heart and heart-lung transplants, 4-15 of liver transplants and 1.2-10.1 of kidney transplants. Although most lymphomas typically occur in lymph nodes, extranodal involvement is also common. The aim of our study was to determine the site and symptoms of PTLD in children who underwent liver transplantation during 2009-2012 in Liver Transplantation Center of Nemazee Hospital. Material and methods: This study is a cohort study on existing data of children who received liver transplant between Juanury 2009 and December 2012 at Liver Transplant Center of Nemazee Hospital in Shiraz. During the study period, the PTLD occurrence was assessed in follow up visits, if the diagnosis of PTLD was confirmed the affected patient was entered the study and additional information was obtained. The diagnosis of PTLD was considered in patients with fever of undetermined origin, lymphadenopathy, allograft dysfunction, and pulmonary infiltrates. The data was analyzed using SPSS software ver.18. Statistical descriptive methods, Chi square test, and independent t-test, were used for analyzing the data. P value smaller than 0.05 were considered significant. Results: Totally, 203 children undergoing liver transplant surgery were evaluated. The age range of patients was 8 months to 18 years with mean of 8.8±5.6 years old. In our study 17 (8.4) patients developed PTLD. The mean interval between transplantation and PTLD diagnosis was 8.4 ±5.61 months ranging from 4 to 24 months. A total of five patients (2.5) died during the follow-up period and all of them were PTLD affected children (29.4 of PTLD patients). Lymph nodes were the most predominant site involved (64.7), while liver and GI involved in 35.2. Conclusion: The results of this study emphasize the relatively high incidence of PTLD after liver transplantation in children. Because of its impact on patient's outcome and reducing recipient's survival, it is important to minimize this problem by early diagnosis and providing effective treatment

Assessment of Airborne Bacterial and Fungal Communities in Shahrekord Hospitals

This paper presents information about airborne microorganisms (bacteria and fungi) in the indoor air of two hospitals (Kashani and Hajar) in the city of Shahrekord, Iran. The settle plate technique using open Petri dishes containing different culture media was employed to collect a sample and using Quick Take 30 Sample Pump three days per week for a period of 8 weeks. Standard microbiological methods were employed for the identification of bacterial and fungal isolates. The results showed that the concentration of bacteria in the study area ranged from 0 to 70 cfu/plate/h, while the concentration of fungi was 0 to 280 cfu/plate/h. Also, 12 bacterial and 3 fungal species were isolated and identified with varying frequencies of occurrence, including Staphylococcus spp., Escherichia coli, Salmonella, Enterobacter, Pseudomonas, Serratia Citrobacter, Proteus, and Klebsiella, while the fungal genera isolated included Yeast, Aspergillus flavus, and Penicillium. While the bacterial isolates Staphylococcus aureus (20.50) and Pseudomonas (9.10) were the most predominant airborne bacteria, yeast (22.70) and Penicillium (20.50) were the most frequently isolated fungal species. The population of microorganisms was the highest during the afternoon. The statistical analysis showed a significant difference between the microbial loads of the two hospitals at P<0.05. The generated data underline the usefulness of monitoring the air quality of the indoor hospital

A model identification approach to quantify impact of whole-body vertical vibrations on limb compliant dynamics and walking stability

Extensive research is ongoing in the field of orthoses/exoskeleton design for efficient lower limbs assistance. However, despite wearable devices reported to improve lower limb mobility, their structural impacts on whole-body vertical dynamics have not been investigated. This study introduced a model identification approach and frequency domain analysis to quantify the impacts of orthosis-generated vibrations on limb stability and contractile dynamics. Experiments were recorded in the motion capture lab using 11 unimpaired subjects by wearing an adjustable ankle–foot orthosis (AFO). The lower limb musculoskeletal structure was identified as spring-mass (SM) and spring-mass-damper (SMD) based compliant models using the whole-body centre-of-mass acceleration data. Furthermore, Nyquist and Bode methods were implemented to quantify stabilities resulting from vertical impacts. Our results illustrated a significant decrease (p < 0.05) in lower limb contractile properties by wearing AFO compared with a normal walk. Also, stability margins quantified by wearing AFO illustrated a significant variance in terms of gain-margins (p < 0.05) for both loading and unloading phases whereas phase-margins decreased (p < 0.05) only for the respective unloading phases. The methods introduced here provide evidence that wearable orthoses significantly affect lower limb vertical dynamics and should be considered when evaluating orthosis/prosthesis/exoskeleton effectiveness

Gait dynamic stability analysis and motor control prediction for varying terrain conditions

This work presents the gait dynamic stability modelling for different walking terrains adopted by the motor. The sensory-motor transitional gait assessment is difficult in clinical environment in case of disorders. The aim of present study was to model and analyse dynamic stability thresholds for gait transitional phases. Experimental data were collected from four healthy subjects while walking on a force platform placed at ramp and level ground walking tracks. The rate-dependent variations in the center of pressure (COP) and ground reaction forces (GRF) were modelled as motor output and input responses. Finite difference and non-linear regression algorithms were implemented to model gait transitions. Dynamic stability estimation for ramp and level ground walking were performed by analysis in time and frequency domains. Our investigation provided interesting results; 1) the overdamped motor output response acts as a compensator for instabilities and oscillations in unloading phase and initial contact, and 2) prediction of ramp ascend walking as the least stable gait than ramp descend for healthy subjects

Rate-dependent gait dynamic stability analysis for motor control estimation

This work presents the gait dynamic stability study for different walking speeds adopted by user intentions. Experimental data were collected from four healthy subjects while walking on a force platform at slow, normal and fast speed. The rate-dependent variations in the center of pressure (COP) and ground reaction forces (GRF) were modelled as motor output and input responses. Finite difference and non-linear regression algorithms were implemented to model gait transitions. Dynamic stability estimation for level ground walking was performed by analysis in time and frequency domains. Study of the COP velocity in loading phase showed that, the overdamped motor output response acts as a compensator for instabilities and oscillations in unloading phase and initial contact. Normal walking was predicted, from gait analysis in frequency domain, as the most stable gait for healthy subjects