Calvin Kuo

Assistant Professor

Research Interests

Sensing human motion
How humans make sense of their own motion
Sensorimotor Neutrophysiology
Wearable Sensing
Musculoskeletal Modeling

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.
 
 

Recruitment

Postdoctoral Fellows

Cardiac arrest sensing and emergency response.

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I am interested in supervising students to conduct interdisciplinary research.

Complete these steps before you reach out to a faculty member!

Check requirements
  • Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
  • Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.
Focus your search
  • Identify specific faculty members who are conducting research in your specific area of interest.
  • Establish that your research interests align with the faculty member’s research interests.
    • Read up on the faculty members in the program and the research being conducted in the department.
    • Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
Make a good impression
  • Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
    • Do not send non-specific, mass emails to everyone in the department hoping for a match.
    • Address the faculty members by name. Your contact should be genuine rather than generic.
  • Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
  • Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
  • Demonstrate that you are familiar with their research:
    • Convey the specific ways you are a good fit for the program.
    • Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
  • Be enthusiastic, but don’t overdo it.
Attend an information session

G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.

 

ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS

These videos contain some general advice from faculty across UBC on finding and reaching out to a supervisor.

Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Adaptability and performance of a wearable fall and near-fall detection system (2022)

Falls in older adults lead to serious physical and mental consequences. An assessment of an older adult’s history of imbalance, characterized by their fall and near-fall history, is an imperative aspect of fall risk assessment. However, cognitive errors during the recall of imbalance events result in an inaccurate capture of fall risk. The detection of falls and near-falls using a wearable sensor system could allow for a quantifiable capture of imbalance for improved risk assessment. However, wearable systems often rely on the placement of sensors on specific locations of the body, limiting their adaptability for users and researchers. This thesis explores the development of an adaptable fall and near-fall detection system that can be deployed in multiple sensor placements. Furthermore, this thesis improves the near-fall detection ability of a multiclass system. Data was collected from 17 participants (9 females, 8 males) simulating 8 types of falls, 7 types of near-falls, and 8 types of activities of daily living. Participants were equipped with three inertial measurement units, placed on a combination of the lower back, thighs, sternum, and arms. The F1-score (harmonic mean of precision and recall) was assessed overall and for each movement category. A support vector machine trained with data aggregated across multiple sensor placements consistently detected imbalance events when tested on various sensor placements (Macro F1-score: 84.64 ± 5.57%). Models trained with data from a specific sensor placement led to decreases in the Macro F1-score of 29.87 ± 7.26% when tested on a different sensor placement. Although the placement-adaptable model demonstrated better overall performance, the detection of near-falls remained relatively poor (Near-Fall F1-score: 50.06 ± 31.42%). An incorporation of data before and after a suspected imbalance event increased the near-fall sensitivity by 31.35 ± 9.62% across multiple sensor placements. Furthermore, the addition of an RUSBoost model reduced class imbalances by screening 82.02% of non-imbalance events prior to classification. In conclusion, the development of an adaptable fall and near-fall detection system requires the collection of data across multiple sensor placements. Incorporation of contextual information can further augment performance, leading to a clinically relevant and user-friendly detection system.

View record

Quantifications and characteristics of dynamic soft tissue artifacts captured by wearable inertial measurement unit sensors (2022)

Inertial measurement units (IMUs) are a prevalent component in wearable sensor systems to study motion in unconstrained environments by measuring both linear acceleration and angular velocity at a point of interest on the human body. However, wearable sensors are often worn on soft tissue that is excited during impulsive impacts, producing dynamic soft tissue artifact measurement errors. This thesis quantifies the error caused by dynamic soft tissue artifacts to IMU sensors with different sensor mass and placement when mild impulsive impacts are delivered to the lower extremity using empirical signal analysis and model-based predictions.Data were collected from three IMU sensors attached at the anterior and posterior center of the dominant shank, and at the back of the ankle of 10 participants (5 females, 5 males). We varied the mass of the two shank IMUs from 5 grams to 37 grams in 8-gram increments. Impacts were generated by dropping the shank freely onto a foam pad placed on a force plate from a height of 21.6 cm.Ground truth vertical linear acceleration measurements from the ankle IMU were first cross-validated against the force plate. We then determined both posterior and anterior IMUs overestimated the ground truth peak vertical linear acceleration measurement by 117.9% and 55.1%, respectively. The posterior IMU had greater overestimations, suggesting more severe dynamic soft tissue artifacts, while errors generally decreased with increasing mass, suggesting a mitigating effect from adding sensor mass. We next assessed post-impact oscillations, which we determined lasted 0.32±0.07 seconds with a frequency of 9.79±2.68 Hz on the posterior and 0.15±0.05 seconds with a frequency of 18.33±12.24 Hz on the anterior. As sensor weight increased, the oscillation duration increased linearly while the frequency decreased for both anterior and posterior IMUs. This suggests that increasing sensor mass amplifies post-impact dynamic soft tissue artifact errors. These results and dependencies were confirmed by modeling dynamic soft tissue artifacts as double spring mass damper systems.In conclusion, the dynamic soft tissue artifacts directly relate to the accuracy of kinematic measurement in IMU sensors with a dependence on sensor mass and placement.

View record

Publications

Current Students & Alumni

This is a small sample of students and/or alumni that have been supervised by this researcher. It is not meant as a comprehensive list.
 

Membership Status

Member of G+PS
View explanation of statuses

Location

Vancouver General Hospital

Program Affiliations

 

If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.

 
 

Planning to do a research degree? Use our expert search to find a potential supervisor!