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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%.

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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.

 

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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.

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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.

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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.

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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.

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A Multi-Modal Person Recognition System for Social Robots

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-03-06
Abstract: 

The paper presents a solution to the problem of person recognition by social robots via a novel brain-inspired multi-modal perceptual system. The system employs spiking neural network to integrate face, body features, and voice data to recognize a person in various social human-robot interaction scenarios. We suggest that, by and large, most reported multi-biometric person recognition algorithms require active participation by the subject and as such are not appropriate for social human-robot interactions. However, the proposed algorithm relaxes this constraint. As there are no public datasets for multimodal systems, we designed a hybrid dataset by integration of the ubiquitous FERET, RGB-D, and TIDIGITS datasets for face recognition, person recognition, and speaker recognition, respectively. The combined dataset facilitates association of facial features, body shape, and speech signature for multimodal person recognition in social settings. This multimodal dataset is employed for testing the algorithm. We assess the performance of the algorithm and discuss its merits against related methods. Within the context of the social robotics, the results suggest the superiority of the proposed method over other reported person recognition algorithms.

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A New Approach to Compute the Porosity and Surface Roughness of Porous Coated Capillary-Assisted Low Pressure Evaporators

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-08-03
Abstract: 

The fundamental characteristics of metal coatings that influence heat transfer are porosity and surface roughness. It is a challenge to analyze the porosity and surface roughness due to the inadequate amount of copper per coated area. In this study, a new approach to non-invasively determine the porosity of metal films utilizing a helium pycnometer and computed micro-tomography (CMT) is presented. Furthermore, a telescope-goniometer is used to measure the surface roughness. Experiments are conducted on four varieties of thin film samples coated with copper powder using wire flame and plasma thermal spray coating methods. The porosities of the thin films were determined to be between 39 and 43%. The thermal spray coating increased the hydrophobicity of the surface and the plasma coating created super-hydrophobic surfaces. The new approach establishes that the porosity of thin films can be non-invasively determined and may also be applied to a wide variety of coated surfaces.

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Makerspaces in First-Year Engineering Education

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-12-27
Abstract: 

Langara College, as one of the leading undergraduate institutions in the province of British Columbia (BC), offers the “Applied Science for Engineering” two-year diploma program as well as the “Engineering Transfer” two-semester certificate program. Three project-based courses are offered as part of the two-year diploma program in Applied Science (APSC) and Computer Science (CPSC) departments: “APSC 1010—Engineering and Technology in Society”, “CPSC 1090—Engineering Graphics”, and “CPSC 1490—Applications of Microcontrollers”, with CPSC 1090 and CPSC 1490 also part of the Engineering Transfer curriculum. Although the goals, scopes, objectives, and evaluation criteria of these courses are different, the main component of all three courses is a group-based technical project. Engineering students have access to Langara College’s Makerspace for the hands-on component of their project. Makerspaces expand experiential learning opportunities and allows students to gain a skillset outside the traditional classroom. This paper begins with a detailed review of the maker movement and the impact of makerspace in higher education. Different forms of makerspace and the benefits of incorporating them on first-year students’ creativity, sense of community, self-confidence, and entrepreneurial skills are discussed. This paper introduces Langara’s engineering program and its project-based design courses. Langara’s interdisciplinary makerspace, its goals and objectives, equipment, and some sample projects are introduced in this paper in detail. We then explain how the group-project component of APSC 1010, CPSC 1090, and CPSC 1490 are managed and how using makerspace improves students’ performance in such projects. In conclusion, the paper describes the evaluation of learning outcomes via an anonymous student survey.

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Validation of Accuracy of SVM-Based Fall Detection System Using Real-World Fall and Non-Fall Datasets

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2017-07-05
Abstract: 

Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings. However, there is a lack of studies examining accuracy in the real-world setting. In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. Five young adults and 19 older adults went about their daily activities while wearing tri-axial accelerometers. Older adults experienced 10 unanticipated falls during the data collection. Approximately 400 hours of activities of daily living were recorded. We employed a machine learning algorithm, Support Vector Machine (SVM) classifier, to identify falls and non-fall events. We found that our system was able to detect 8 out of the 10 falls in older adults using signals from a single accelerometer (waist or sternum). Furthermore, our system did not report any false alarm during approximately 28.5 hours of recorded data from young adults. However, with older adults, the false positive rate among individuals ranged from 0 to 0.3 false alarms per hour. While our system showed higher fall detection and substantially lower false positive rate than the existing fall detection systems, there is a need for continuous efforts to collect real-world data within the target population to perform fall validation studies for fall detection systems on bigger real-world fall and non-fall datasets.

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