Mechatronics Systems Engineering, School of

Receive updates for this collection

Numerical Design of a Guide Vane for an Axial Fan

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-07-01
Abstract: 

This study presents a custom guide vane design to substantially reduce the waste energy in axial fans. Ideally, there is no velocity component in the radial direction in axial fans, but in practice, the air leaving the axial fan has a large tangential component of velocity which produces a large amount of swirl kinetic energy. In order to solve this problem, a guide vane is designed to remove the rotational component of the air. The methodology described in this project is based on the fundamental governing continuity, momentum, and energy equations using the Finite Volume Method (FVM). In this project, the standard k-ω model is used for turbulent modeling. Two dimensional (2D) geometry of blades and airflow cross-section are designed using AutoCAD and CATIA while GAMBIT is employed to generate a suitable mesh for the three dimensional (3D) model. The mesh independence test is done to analyze the performance. The axial fan is simulated using FLUENT software to prove an increase in airflow rate after using the guide vane. Considering the final results, it can be observed that the airflow is increased up to 6.3%.

Document type: 
Article
File(s): 

Natural Graphite Sheet Heat Sinks with Embedded Heat Pipes

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-04-20
Abstract: 

Natural graphite sheet (NGS) is a candidate material for lightweight, high-performance heat sinks. We show that the low through–plane thermal conductivity can be mitigated by using heat pipes. In the measured configuration, the thermal resistance of an NGS heat sink with embedded heat pipes is comparable to that of a geometrically-identical aluminum one. The achieved weight reduction is 37 %. When electrical insulation of a heat sink is not required, soft and conforming NGS does not require thermal grease at the interface between the heat source and the heat sink. The low electrical conductivity of NGS does not lead to a decrease in common mode conducted emissions, but the potential to reduce the radiated emissions was quantified to be 12 to 97 % based on an analogy with antennas. In practical applications, replacing an existing heat sink with a geometrically identical NGS one is not recommended because it limits the achievable improvements in thermal performance, weight, and cost. Instead, we suggest using an optimization algorithm to determine the optimal heat sink geometry.

Document type: 
Article
File(s): 

A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-04-27
Abstract: 

In this paper, we propose a novel algorithm to detect a door and its orientation in indoor settings from the view of a social robot equipped with only a monocular camera. The challenge is to achieve this goal with only a 2D image from a monocular camera. The proposed system is designed through the integration of several modules, each of which serves a special purpose. The detection of the door is addressed by training a convolutional neural network (CNN) model on a new dataset for Social Robot Indoor Navigation (SRIN). The direction of the door (from the robot’s observation) is achieved by three other modules: Depth module, Pixel-Selection module, and Pixel2Angle module, respectively. We include simulation results and real-time experiments to demonstrate the performance of the algorithm. The outcome of this study could be beneficial in any robotic navigation system for indoor environments.

Document type: 
Article
File(s): 

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-03-07
Abstract: 

One in three adults worldwide has hypertension, which is associated with significant morbidity and mortality. Consequently, there is a global demand for continuous and non-invasive blood pressure (BP) measurements that are convenient, easy to use, and more accurate than the currently available methods for detecting hypertension. This could easily be achieved through the integration of single-site photoplethysmography (PPG) readings into wearable devices, although improved reliability and an understanding of BP estimation accuracy are essential. This review paper focuses on understanding the features of PPG associated with BP and examines the development of this technology over the 2010–2019 period in terms of validation, sample size, diversity of subjects, and datasets used. Challenges and opportunities to move single-site PPG forward are also discussed.

Document type: 
Article
File(s): 

Machine Learning Ranks ECG as an Optimal Wearable Biosignal for Assessing Driving Stress

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-02-19
Abstract: 

The demand for wearable devices that can detect anxiety and stress when driving is increasing. Recent studies have attempted to use multiple biosignals to detect driving stress. However, collecting multiple biosignals can be complex and is associated with numerous challenges. Determining the optimal biosignal for assessing driving stress can save lives. To the best of our knowledge, no study has investigated both longitudinal and transitional stress assessment using supervised and unsupervised ML techniques. Thus, this study hypothesizes that the optimal signal for assessing driving stress will consistently detect stress using supervised and unsupervised machine learning (ML) techniques. Two different approaches were used to assess driving stress: longitudinal (a combined repeated measurement of the same biosignals over three driving states) and transitional (switching from state to state such as city to highway driving). The longitudinal analysis did not involve a feature extraction phase while the transitional analysis involved a feature extraction phase. The longitudinal analysis consists of a novel interaction ensemble (INTENSE) that aggregates three unsupervised ML approaches: interaction principal component analysis, connectivity-based clustering, and K-means clustering. INTENSE was developed to uncover new knowledge by revealing the strongest correlation between the biosignal and driving stress marker. These three MLs each have their well-known and distinctive geometrical basis. Thus, the aggregation of their result would provide a more robust examination of the simultaneous non-causal associations between six biosignals: electrocardiogram (ECG), electromyogram, hand galvanic skin resistance, foot galvanic skin resistance, heart rate, respiration, and the driving stress marker. INTENSE indicates that ECG is highly correlated with the driving stress marker. The supervised ML algorithms confirmed that ECG is the most informative biosignal for detecting driving stress, with an overall accuracy of 75.02%.

Document type: 
Article
File(s): 

Manual Wheelchair Downhill Stability: An Analysis of Factors Affecting Tip Probability

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-11-06
Abstract: 

Background  For people who use manual wheelchairs, tips and falls can result in serious injuries including bone fractures, concussions, and traumatic brain injury. We aimed to characterize how wheelchair configuration changes (including on-the-fly adjustments), user variables, and usage conditions affected dynamic tip probability while rolling down a slope and contacting a small block.

Methods  Rigid body dynamic models of a manual wheelchair and test dummy were created using multi-body software (Madymo, TASS International, Livonia, MI), and validated with 189 experiments. Dynamic stability was assessed for a range of seat angles (0 to 20° below horizontal), backrest angles (0 to 20°), rear axle positions (0 to 20 cm from base of backrest), ground slopes (0 to 15°), bump heights (0 to 4 cm), wheelchair speeds (0 to 20 km/hr), user masses (50 to 115 kg), and user positions (0 to 10 cm from base of backrest). The tip classifications (forward tip, backward tip, rolled over bump, or stopped by bump) were investigated using a nominal logistic regression analysis.

Results  Faster wheelchair speeds significantly increased the probability of tipping either forward or backward rather than stopping, but also increased the probability of rolling over the bump (p < 0.001). When the rear axle was positioned forward, this increased the risk of a backward tip compared to all other outcomes (p < 0.001), but also reduced the probability of being stopped by the bump (p < 0.001 compared to forward tip, p < 0.02 compared to rolling over). Reclining the backrest reduced the probability of a forward tip compared to all other outcomes (p < 0.001), and lowering the seat increased the probability of either rolling over the bump or tipping backwards rather than tipping forward (p < 0.001). In general, the wheelchair rolled over bumps < 1.5 cm, and forwards tipping was avoided by reducing the speed to 1 km/hr.

Conclusions  The probability of forward tipping, corresponding to the greatest risk of injury, was significantly reduced for decreased speeds, smaller bumps, a reclined backrest, and a lower rear seat height. For wheelchairs with dynamic seating adjustability, when travelling downhill, on-the-fly adjustments to the seat or backrest can increase the likelihood of safely rolling over a bump.

Background  For people who use manual wheelchairs, tips and falls can result in serious injuries including bone fractures, concussions, and traumatic brain injury. We aimed to characterize how wheelchair configuration changes (including on-the-fly adjustments), user variables, and usage conditions affected dynamic tip probability while rolling down a slope and contacting a small block.

Methods  Rigid body dynamic models of a manual wheelchair and test dummy were created using multi-body software (Madymo, TASS International, Livonia, MI), and validated with 189 experiments. Dynamic stability was assessed for a range of seat angles (0 to 20° below horizontal), backrest angles (0 to 20°), rear axle positions (0 to 20 cm from base of backrest), ground slopes (0 to 15°), bump heights (0 to 4 cm), wheelchair speeds (0 to 20 km/hr), user masses (50 to 115 kg), and user positions (0 to 10 cm from base of backrest). The tip classifications (forward tip, backward tip, rolled over bump, or stopped by bump) were investigated using a nominal logistic regression analysis.

Results  Faster wheelchair speeds significantly increased the probability of tipping either forward or backward rather than stopping, but also increased the probability of rolling over the bump (p < 0.001). When the rear axle was positioned forward, this increased the risk of a backward tip compared to all other outcomes (p < 0.001), but also reduced the probability of being stopped by the bump (p < 0.001 compared to forward tip, p < 0.02 compared to rolling over). Reclining the backrest reduced the probability of a forward tip compared to all other outcomes (p < 0.001), and lowering the seat increased the probability of either rolling over the bump or tipping backwards rather than tipping forward (p < 0.001). In general, the wheelchair rolled over bumps < 1.5 cm, and forwards tipping was avoided by reducing the speed to 1 km/hr.

Conclusions  The probability of forward tipping, corresponding to the greatest risk of injury, was significantly reduced for decreased speeds, smaller bumps, a reclined backrest, and a lower rear seat height. For wheelchairs with dynamic seating adjustability, when travelling downhill, on-the-fly adjustments to the seat or backrest can increase the likelihood of safely rolling over a bump.

 

Document type: 
Article
File(s): 

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-07-31
Abstract: 

Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect if the user is talking as a future indicator of social interaction. In a laboratory study, fifteen healthy young participants were asked to talk while performing daily activities such as sitting, standing and walking. It is known that the breathing pattern differs significantly between normal and speech breathing (i.e., talking). We integrated resistive stretch sensors into wearable elastic bands, with a future integration into clothing in mind, to record the expansion and contraction of the chest and abdomen while breathing. We developed an algorithm incorporating machine learning and evaluated its performance in distinguishing between periods of talking and non-talking. In an intra-subject analysis, our algorithm detected talking with an average accuracy of 85%. The highest accuracy of 88% was achieved during sitting and the lowest accuracy of 80.6% during walking. Complete segments of talking were correctly identified with 96% accuracy. From the evaluated machine learning algorithms, the random forest classifier performed best on our dataset. We demonstrate that wearable textile-based sensors in combination with machine learning can be used to detect when the user is talking. In the future, this approach may be used as an indicator of social interaction to prevent social isolation and loneliness.

Document type: 
Article
File(s): 

Perceptions of Senior Citizens on the Use and Desired Features of a Wristband for Maintaining, Strengthening, and Regaining Hand and Finger Function

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-02-06
Abstract: 

The objective of this study was to understand whether seniors would wear a wristband technology to help them improve, retain, regain, or strengthen hand and finger function and to gather information about the desired features of the technology to enhance compliance in use. The strength and functioning of the hand and fingers decrease as people age and can have a detrimental impact on the individual’s quality of life. Studies have shown that regular exercise of the hands can help the individual maintain hand strength and improve function. Two self-reported, online questionnaires were designed and administered to seniors. Of the 105 surveyed, 62% indicated they would wear a wristband. The top desired wristband features identified were ease of putting the device on, unobtrusiveness and comfort of the device with a desired price point of $99 or less. The majority of seniors surveyed were interested in wearing the wristband; however, results revealed that the wristband would need to be tailored for this population for use and uptake of the wristband. The results of this study provide insight into the features and functionalities of a wristband that would enhance user compliance in seniors who wished to improve hand and finger function.

Document type: 
Article
File(s): 

A Brain-Inspired Multi-Modal Perceptual System for Social Robots: An Experimental Realization

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-06-29
Abstract: 

We propose a multi-modal perceptual system that is inspired by the inner working of the human brain; in particular, the hierarchical structure of the sensory cortex and the spatial-temporal binding criteria. The system is context independent and can be applied to many on-going problems in social robotics, including but not limited to person recognition, emotion recognition, and multi-modal robot doctor to name a few. The system encapsulates the parallel distributed processing of real-world stimuli through different sensor modalities and encoding them into features vectors which in turn are processed via a number of dedicated processing units (DPUs) through hierarchical paths. DPUs are algorithmic realizations of the cell assemblies in neuroscience. A plausible and realistic perceptual system is presented via the integration of the outputs from these units by spiking neural networks. We will also discuss other components of the system including top-down influences and the integration of information through temporal binding with fading memory and suggest two alternatives to realize these criteria. Finally, we will demonstrate the implementation of this architecture on a hardware platform as a social robot and report experimental studies on the system.

Document type: 
Article
File(s): 

A Wearable Gait Phase Detection System Based on Force Myography Techniques

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-04-21
Abstract: 

(1) Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals’ gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2) Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3) Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4) Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems.

Document type: 
Article
File(s):