“Picking up a fragile object, like an egg, can turn into a monumental task”: Improving perceptions of grip strength in prosthetic users

(Photos provided by Lincoln Inglis)

“This project was especially interesting because it allowed me to teach myself a number of new skills that a traditional psychology project wouldn’t.”  ~Lincoln Inglis

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*In the following article, TS refers to The Synapse and LI refers to Lincoln Inglis*

TS: Could you briefly introduce yourself, your supervisor, the topic of your thesis, and the field it contributes to?

LI: I’m Lincoln Inglis, and I’ve just completed my Bachelor of Science with Honours in Psychology and a minor in Computer Science at Acadia University. My honours thesis was done under the supervision of Dr. Dan Blustein, and is in the field of computational neuroscience.

My thesis is on the development and assessment of augmented reality (i.e. additional sensory information that accompanies our perceived reality) that attempts to improve how grip and force is perceived in a prosthesis. 

Individuals with upper limb prosthetic devices lack the sensory neurons required to truly determine the force they’re applying to a held object. As such, picking up a fragile object, like an egg, can turn into a monumental task. This project sought to create a unique augmented reality (AR) system that lets users gauge their applied grip strength through a colour-changing sphere on their prosthetic hand, which is perceived by wearing an AR headset. 

TS: What is the inspiration for your study, and your research question(s)? Does your study relate to fields other than psychology?

LI: The project itself was conceptualized by my supervisor, and my main source of motivation was simply that it sounded like a fun and fulfilling project to work on (plus it would let me play around with a fancy AR headset). It was heavily related to my minor in computer science, since it required developing an entire AR app. Some of my participants also likened it to kinesiology, since it somewhat looked at how well users could learn to control the prosthetic hand using muscles in their forearm.

There are a number of methods we could use to help convey grip strength information to prosthesis users. For example, we could try surgical interventions that attempt to actually replicate the sensation of touch in the brain, but these tend to be fairly invasive. We might instead think to simulate touch to what remains of the user’s naturalistic arm, such as implementing tactile vibrations that increase in intensity with greater grip strength. But, as we move that sensation further away from where it is naturally felt on our body - in this case, on the fingers - it will be less efficient, meaning that this method might not work well for those missing their entire arm. We know that vision plays a large role in predicting grip strength, so we could try conveying this information visually. AR is a great way to do this. 

The application I developed for this project tracks the user's prosthetic hand and shows a large sphere in the centre of it that changes colour to represent grip strength. Our question was if this method was able to improve users’ motor learning and performance when trying to hold fragile objects.

TS: Could you provide a brief summary of the methods or experimental procedure used in this study?

LI: This study tested how able-bodied, undergraduate users performed on a grasp-and-lift task using a prosthesis. Participants were randomly assigned to one of two conditions, with one group receiving AR feedback from the developed application, and the other wearing a powered-off AR headset. The grasp-and-lift task involved trying to hold a “mechanical egg” - a small origami cube held internally intact by a piece of raw pasta - for two seconds without breaking or dropping it. Participants completed three training blocks, with each block consisting of 20 egg grasps. We measured the total number of eggs broken or dropped in each training block.

TS: What are the main results you observed or expect to see?

LI: We failed to find evidence that the application improved performance, or that motor learning occurred across either of the procedures. However, these results could provide insight into how task difficulty changes the information we use to complete them. Prior research finds that AR only seems to help in more difficult tasks, such as picking up and moving around an object, as opposed to holding it in place as in our experiment. These results may align with that finding and suggest that evaluating our method requires a harder procedure. 

TS: What would you consider the most intriguing part of the research process?

LI: With this project in particular, I always had fun finding weird ways to make the system as a whole work. I don’t consider myself a particularly talented programmer, so app development required a lot of creativity. My favourite example is how I actually got the grip strength information from the prosthesis to the application that generates the coloured sphere on the prosthetic hand to visually convey grip strength. The headset we used (the HoloLens 2), through which the coloured sphere was perceived, typically works by being hooked up to things like keyboards. Therefore, my solution to the data relay problem was to trick the headset into thinking that the prosthetic hand was actually a keyboard, which simply involved typing the data it needed at a very fast rate!

TS: Is there anything else you wish to share about the research experience?

LI: This project was especially interesting because it allowed me to teach myself a number of new skills that a traditional psychology project wouldn’t. None of the languages I was programming in were familiar to me, so I was able to add them to my repertoire. At one point, our data relay system required a Bluetooth chip, which forced me to learn how circuits worked - a concept that was foreign even to my computer scientist colleagues. Working on interdisciplinary research like this not only allows us to answer scientific questions, but allows us to build our skills in a variety of domains. Ψ

Created for The Synapse by Incé Husain.