Emneord:
Machine Learning,
Deep learning,
Human-computer interaction,
Biomedical Engineering,
Artificial Intelligence,
Reinforcement Learning
Publikasjoner
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Rahman, Md Abdur; Bhuiyan, Md Tofael Ahmed; Khan, Md Fokrul Islam; Uddin, Md Zia; Côté-Allard, Ulysse Teller Masao & Masum, Abdul Kadar Muhammad
(2026).
Integrating domain-specific convolutional features with Swin Transformers for transparent cervical cancer diagnostics.
Frontiers in Oncology.
16.
doi:
10.3389/fonc.2026.1878677.
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Arnim, Hugh Alexander von; Erdem, Cagri; Côté-Allard, Ulysse Teller Masao & Jensenius, Alexander Refsum
(2025).
A Sensor is not a Sensor: Diffracting the Preservation of Sonic Microinteraction with the SiFiBand.
I Seiça, Mariana & Wirfs-Brock, Jordan (Red.),
AM '25: Proceedings of the 20th International Audio Mostly Conference.
Association for Computing Machinery (ACM).
ISSN 9798400720659.
s. 318–325.
doi:
10.1145/3771594.3771626.
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This paper documents our exploratory work to preserve the interactive music system Stillness Under Tension—developed to explore inverse sonic microinteraction—by porting it from the original and discontinued Myo sensor armband to SiFiBand, a new prototype armband with motion (IMU) and muscle (EMG) sensors. We approach this by merging the Multilevel Dynamic Preservation model with a “diffraction-in-action” method grounded in a theoretical entanglement perspective. Rather than focusing on the Myo version’s artefactual remains, we explore the difference in data representations offered by the two devices as our point of departure. The paper describes the sensor devices, evaluating their data representations given their technical specifications, and describing how these differences propagate throughout our attempt to preserve the system, enacting necessary changes. We discuss the implications of merging these methods in view of the long-term preservation of interactive music systems. Our version 2.0 of Stillness Under Tension finds itself experientially in a position between familiarity and newness.
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Eddy, Ethan; Campbell, Evan; Côté-Allard, Ulysse Teller Masao; Bateman, Scott & Scheme, Erik
(2024).
Discrete Gesture Recognition Using Multimodal PPG, IMU, and Single-Channel EMG Recorded at the Wrist.
IEEE Sensors Letters.
8(9).
doi:
10.1109/LSENS.2024.3447240.
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Campbell, Evan; Eddy, Ethan; Isabel, Xavier; Bateman, Scott; Gosselin, Benoit & Côté-Allard, Ulysse Teller Masao
[Vis alle 7 forfattere av denne artikkelen]
(2024).
Screen Guided Training Does Not Capture Goal-Oriented Behaviours: Learning Myoelectric Control Mappings From Scratch Using Context Informed Incremental Learning.
IEEE transactions on neural systems and rehabilitation engineering.
ISSN 1534-4320.
doi:
10.1109/TNSRE.2024.3518059.
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Human-machine interfaces based on myoelectric signals typically use screen-guided training (SGT) for model calibration, but this approach fails to capture realistic user behaviors. This study evaluates a user-in-the-loop context-informed incremental learning (CIIL) framework, comparing SGT, SGT followed by CIIL adaptation (SGT-A), and a novel zero-shot adaptation (ZS-A) CIIL approach that begins adapting with no prior training. Sixteen participants completed a Fitts’ Law targeting task using these control schemes, with performance measured via online throughput and offline classification accuracy. Despite lower offline accuracy, the ZS-A model achieved the highest online throughput ( 1.47 ± 0.46 bits/s), significantly outperforming the SGT baseline ( 1.15 ± 0.37 bits/s) and reached competitive performance within 200 seconds. To further enhance control performance, a novel adaptive sigmoid-based proportional control mapping was introduced, dynamically adjusting control signals to allow precise control near neutral positions and rapid movements at higher activation levels, better aligning with natural user behaviors. These findings demonstrate that CIIL can surpass traditional SGT methods in online performance and emphasize the value of real-time user-in-the-loop data for developing adaptable and intuitive myoelectric interfaces, with implications for prosthetics, rehabilitation, and telerobotics.
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Campbell, Evan; Eddy, Ethan; Bateman, Scott; Côté-Allard, Ulysse Teller Masao & Scheme, Erik
(2024).
Context-informed incremental learning improves both the performance and resilience of myoelectric control.
Journal of NeuroEngineering and Rehabilitation.
21(1).
doi:
10.1186/s12984-024-01355-4.
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Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model’s parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control.
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Jakobsen, Petter; Côté-Allard, Ulysse Teller Masao; Riegler, Michael; Stabell, Lena Antonsen; Stautland, Andrea & Nordgreen, Tine
[Vis alle 9 forfattere av denne artikkelen]
(2024).
Early warning signals observed in motor activity preceding mood state change in bipolar disorder.
Bipolar Disorders.
ISSN 1398-5647.
26(5),
s. 468–478.
doi:
10.1111/bdi.13430.
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Intro
Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes.
Methods
Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants’ mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes.
Results
Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 hours/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days.
Conclusion
The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder.
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Poirier, Samuel; Côté-Allard, Ulysse Teller Masao; Routhier, François & Campeau-Lecours, Alexandre
(2023).
Efficient Self-Attention Model for Speech Recognition-Based Assistive Robots Control.
Sensors.
23(13).
doi:
10.3390/s23136056.
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Assistive robots are tools that people living with upper body disabilities can leverage to autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control methods still rely on low-dimensional, easy-to-implement interfaces such as joysticks that tend to be unintuitive and cumbersome to use. In contrast, vocal commands may represent a viable and intuitive alternative. This work represents an important step toward providing a viable vocal interface for people living with upper limb disabilities by proposing a novel lightweight vocal command recognition system. The proposed model leverages the MobileNet2 architecture, augmenting it with a novel approach to the self-attention mechanism, achieving a new state-of-the-art performance for Keyword Spotting (KWS) on the Google Speech Commands Dataset (GSCD). Moreover, this work presents a new dataset, referred to as the French Speech Commands Dataset (FSCD), comprising 4963 vocal command utterances. Using the GSCD as the source, we used Transfer Learning (TL) to adapt the model to this cross-language task. TL has been shown to significantly improve the model performance on the FSCD. The viability of the proposed approach is further demonstrated through real-life control of a robotic arm by four healthy participants using both the proposed vocal interface and a joystick.
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Lamo, Yngve; Mukhiya, Suresh Kumar; Rabbi, Fazle; Aminifar, Amin; Lillehaug, Svein Ivar & Tørresen, Jim
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(2022).
Towards adaptive technology in routine mental health care.
Digital Health.
8,
s. 1–16.
doi:
10.1177/20552076221128678.
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This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients’ needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.
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Jakobsen, Petter; Stautland, Andrea; Riegler, Michael; Côté-Allard, Ulysse Teller Masao; Sepasdar, Zahra & Nordgreen, Tine
[Vis alle 9 forfattere av denne artikkelen]
(2022).
Complexity and variability analyses of motor activity distinguish mood states in Bipolar Disorder.
PLOS ONE.
17(1),
s. 1–19.
doi:
10.1371/journal.pone.0262232.
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Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia.
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Côté-Allard, Ulysse Teller Masao; Pham, Hoang Minh; Schultz, Alexandra K.; Nordgreen, Tine & Tørresen, Jim
(2022).
Adherence Forecasting for Guided Internet-Delivered Cognitive Behavioral Therapy: A Minimally Data-Sensitive Approach.
IEEE Journal of Biomedical and Health Informatics.
ISSN 2168-2194.
27(6),
s. 2771–2781.
doi:
10.1109/JBHI.2022.3204737.
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Côté-Allard, Ulysse; Gagnon-Turcotte, Gabriel; Phinyomark, Angkoon; Glette, Kyrre Harald; Scheme, Erik & Laviolette, François
[Vis alle 7 forfattere av denne artikkelen]
(2021).
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition.
IEEE transactions on neural systems and rehabilitation engineering.
ISSN 1534-4320.
doi:
10.1109/TNSRE.2021.3059741.
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Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly ( p<0.05 ) outperforms using fine-tuning as the recalibration technique.
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Nordgreen, Tine; Rabbi, Fazle; Tørresen, Jim; Skar, Yngvar Sigmund; Guribye, Frode & Inal, Yavuz
[Vis alle 23 forfattere av denne artikkelen]
(2021).
Challenges and possible solutions in cross-disciplinary and cross-sectorial research teams within the domain of e-mental health.
Journal of Enabling Technologies (JET).
ISSN 2398-6263.
15(4),
s. 241–251.
doi:
10.1108/JET-03-2021-0013.
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Mental illness presents a huge individual, societal and economic challenges, currently accounting for 20% of the worldwide burden of disease. There is a gap between the need for and access to services. Digital technology has been proven effective in e-mental health for preventing and treating mental health problems. However, there is a need for cross-disciplinary efforts to increase the impact of e-mental health services. This paper aims to report key challenges and possible solutions for cross-disciplinary and cross-sectorial research teams within the domain of e-mental health.
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Côté-Allard, Ulysse; Campbell, Evan; Phinyomark, Angkoon; Laviolette, François; Gosselin, Benoit & Scheme, Erik
(2020).
Interpreting deep learning features for myoelectric control: A comparison with handcrafted features.
Frontiers in Bioengineering and Biotechnology.
doi:
10.3389/fbioe.2020.00158.
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Côté-Allard, Ulysse; Gagnon-Turcotte, Gabriel; Phinyomark, Angkoon; Glette, Kyrre; Scheme, Erik & Laviolette, Francois
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(2020).
Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition.
IEEE Access.
8,
s. 177941–177955.
doi:
10.1109/ACCESS.2020.3027497.
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Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system’s performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.
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Côté-Allard, Ulysse; Gagnon-Turcotte, Gabriel; Laviolette, François & Gosselin, Benoit
(2019).
A low-cost, wireless, 3-d-printed custom armband for sEMG hand gesture recognition.
Sensors.
19(12).
doi:
10.3390/s19122811.
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Campeau-Lecours, Alexandre; Côté-Allard, Ulysse; Vu, Dinh-Son; Routhier, François; Gosselin, Benoit & Gosselin, Clément
(2018).
Intuitive adaptive orientation control for enhanced human–robot interaction.
IEEE Transactions on Robotics.
ISSN 1552-3098.
35(2),
s. 509–520.
doi:
10.1109/TRO.2018.2885464.
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Côté-Allard, Ulysse; Fall, Cheikh Latyr; Campeau-Lecours, Alexandre; Gosselin, Clément; Laviolette, François & Gosselin, Benoit
(2017).
Transfer learning for sEMG hand gestures recognition using convolutional neural networks.
IEEE International Conference on Systems, Man and Cybernetics (SMC).
ISSN 1062-922X.
s. 1663–1668.
doi:
10.1109/SMC.2017.8122854.
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Boeker, Matthias; Swarbrick, Dana; Côté-Allard, Ulysse Teller Masao; P., Adam, Marc T.; Hammer, Hugo Lewi & Halvorsen, Pål
(2026).
The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing.
doi:
https:/arxiv.org/pdf/2603.26575.
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Abstract page for arXiv paper 2603.26575:
Climbing is a multifaceted sport that combines physical demands
and emotional and cognitive challenges. Ascent styles differ in fall
distance with lead climbing involving larger falls than top rope climb-
ing, which may result in different perceived risk and fear. In this
study, we investigated the psychophysiological relationship between
perceived fear and muscle activity in climbers using a combination
of statistical modeling and deep learning techniques. We conducted
an experiment with 19 climbers, collecting electromyography (EMG),
electrocardiography (ECG) and arm motion data during lead and top
rope climbing. Perceived fear ratings were collected for the different
phases of the climb. Using a linear mixed-effects model, we analyzed
the relationships between perceived fear and physiological measures.
To capture the non-linear dynamics of this relationship, we extended
our analysis to deep learning models and integrated random effects for
a personalized modeling approach. Our results showed that random
effects improved model performance of the mean squared error (MSE),
mean absolute error (MAE) and root mean squared error (RMSE).
The results showed that muscle fatigue correlates significantly with
increased fear during lead climbing. This study highlights the potential
of combining statistical and deep learning approaches for modeling
the interplay between psychological and physiological states during
climbing.
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Arnim, Hugh Alexander von; Erdem, Cagri; Côté-Allard, Ulysse Teller Masao & Jensenius, Alexander Refsum
(2025).
A Sensor is not a Sensor: Diffracting the Preservation of Sonic Microinteraction with the SiFiBand.
Fulltekst i vitenarkiv
Vis sammendrag
This paper documents our exploratory work to preserve the interactive music system Stillness Under Tension—developed to explore inverse sonic microinteraction—by porting it from the original and discontinued Myo sensor armband to SiFiBand, a new prototype armband with motion (IMU) and muscle (EMG) sensors. We approach this by merging the Multilevel Dynamic Preservation model with a “diffraction-in-action” method grounded in a theoretical entanglement perspective. Rather than focusing on the Myo version’s artefactual remains, we explore the difference in data representations offered by the two devices as our point of departure. The paper describes the sensor devices, evaluating their data representations given their technical specifications, and describing how these differences propagate throughout our attempt to preserve the system, enacting necessary changes. We discuss the implications of merging these methods in view of the long-term preservation of interactive music systems. Our version 2.0 of Stillness Under Tension finds itself experientially in a position between familiarity and newness.
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Solbakk, Anne-Kristin; Endestad, Tor; Bégel, Valentin & Côté-Allard, Ulysse Teller Masao
(2024).
Novel approaches for unraveling biological and behavioral rhythms in human development.
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Côté-Allard, Ulysse Teller Masao
(2021).
Long-Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-worn Sensors.
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Côté-Allard, Ulysse Teller Masao
(2021).
Adherence Forecasting for G-ICBT. The INTROMAT closing seminar 2021.
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Publisert
21. aug. 2023 14:02
- Sist endret
22. okt. 2025 12:30
