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Senast inlagda
Design and Development of a Novel Sensorized Orthosis for Orthopedic Rehabilitation
(2026) Zhou, Yuhong
This thesis presents the design, development, and implementation of a novel sensorized
orthosis for orthopedic rehabilitation. The system addresses the lack of
quantitative feedback in traditional passive braces by integrating compact sensing
and data communication modules into a lightweight and modular structure. An
absolute rotary encoder and two six-axis IMUs were employed to measure knee joint
angle and motion dynamics in real time. The embedded controller, based on an
ESP32-S3 microcontroller, supports high-frequency data acquisition, local microSD
logging, and wireless communication with the ROS 2 framework, enabling both
offline analysis and interactive applications.
The system was designed as a fully independent and non-invasive add-on to a commercial
postoperative brace, maintaining clinical compatibility while adding sensing
capability. The complete system weighs approximately 180 grams and allows rapid
attachment and removal without altering the brace’s mechanical properties. Experimental
validation confirmed stable and synchronized sensor performance, accurate
joint-angle estimation, and reliable differentiation between correct and incorrect rehabilitation
motions.
The integration of the existing Unity3D-based rehabilitation game with the proposed
system successfully validated its feasibility and immense potential in intelligent,
interactive rehabilitation. This system bridges the technological gap between
conventional orthopedic braces and intelligent robotic rehabilitation devices, establishing
a solid foundation for achieving intelligent, quantitative, and personalized
rehabilitation assessment and training.
Driver assistance for steering while reversing long combination vehicles
(2025) Sreeram, Anirudh; Lu, Chaojie; Pidur Kuppusamy, Harshith; Zhao, Junyu; Mehta, Nihar Prakash; Huang, Zichen
Long combination vehicles, such as A-double configurations, pose significant challenges during low-speed reversing
due to their length and multiple articulation points, which increase the risk of instability and jackknifing.
This project investigates a driver-assist approach for reverse steering of such vehicles, aiming to improve controllability
during low-speed manoeuvres.
A desktop simulation framework was developed using TruckMaker for vehicle modelling and MATLAB/Simulink
for control implementation and visualisation. The reversing behaviour was described using a kinematic singletrack
model for articulated vehicles, and an LQR-based feedback controller was designed to stabilise articulation
angles and regulate the reversing trajectory. Driver input is provided through a knob-based interface specifying
the desired turning radius, which is used to calculate steering commands for the tractor and the rear trailer.
The system was evaluated in simulation through multiple reversing scenarios, including straight-line, constantradius,
and misaligned initial conditions, demonstrating stable reversing behaviour without jackknifing and
consistent path tracking under the tested conditions. In addition, a CAN communication framework was
implemented to explore real-vehicle integration. While vehicle state signals were successfully received in real
time, steering command transmission could not yet be validated due to gateway limitations. The project
establishes a simulation and HIL-ready framework for future refinement and real-vehicle validation.
Exploring and testing of orthopedic exoskeletons
(2026) Le Veau, Christopher; Mahdavi, Amir
Exoskeletons are a growing technology in both industry and healthcare. With the increasing
number of cases of musculoskeletal disorders, exoskeletons can be a potential solution for the
rehabilitation of people with such disorders.
The department of electrical engineering at Chalmers University of Technology, working with
Sahlgrenska University Hospital, wants to develop the understanding and technology of
exoskeletons. A previous student group of Chalmers developed an elbow exoskeleton for this
task, which used the pre-contracted exoskeleton EduExo lite 2 from AUXIVO.
The goal of this project is to improve the movement of the previous student group’s elbow
exoskeleton, as its movement system was too unnatural and static. This was done by improving
and developing a more dynamic movement system for the elbow exoskeleton, to further
develop understanding and research of orthopedic exoskeletons for rehabilitation, without
introducing new hardware components or replacing the existing hardware components provided
by the group.
Developing the movement system required first testing the learning the response time between
the microcontroller and feedback servomotor, as well as the rotation speed and resolution of
the feedback servomotor. Spline and polynomial functions were used to better simulate the
natural movement of a human arm, as it gives motion a gradual increase and decrease in
movement velocity.
The resulting code made it a dynamic movement control system, where movement from any
start and end point, results in a smoother and softer motion, where the duration of the movement
can also be altered.
The result of this project provides good grounds for potential further development, which can
lead to a dynamic modular movement system to provide ways easily adapted to a patient’s
specific needs.
Exploring the Opportunities for Reuse in Infrastructure Projects; A Case Study of a Leading Construction Company in Sweden
(2025) Dires, Hana; Honarkar, Arian
The construction sector is a significant contributor to resource depletion, greenhouse gas
emissions, and construction waste, underscoring the need for more sustainable infrastructure
development. This study investigates the feasibility of material reuse in infrastructure by
examining its key enablers, barriers, and practical pathways for implementation. A qualitative
research design was employed, combining a literature review with a case study of a large-scale
infrastructure project in Sweden, augmented by semi-structured interviews with stakeholders. The
analysis reveals that integrating reuse considerations into the early design phase is pivotal,
reinforced by supporting policies, technical capacity, market readiness, collaborative networks,
and cultural acceptance. Notable barriers include the lack of standardized guidelines, inconsistent
material quality, and logistical challenges related to storage, transport, and supply-demand
coordination. The proposed framework emphasizes early design as the foundation for facilitating
reuse, supported by regulatory reform, digital tracking tools, and policy incentives to stimulate
adoption. This integrated approach provides a viable route for embedding circular economy
practices in infrastructure, with promising environmental and economic benefits for the built
environment.
Semantically Aware Attacks on Text-based Models: An Extension of Context-aware and Neighbourhood Comparisonbased Membership Inference Attacks
(2025) Glänte, Gabriel
Training deep-learning models requires large amounts of data. When this data is sensitive, e.g., containing personal information, it is important to ensure that no sensitive information can be extracted from the trained models. In a membership inference attack (MIA), an adversary is expected to have access to a trained model θ and a data sample d, sampled from the same distribution as the unknown training data. The objective of the adversary is to construct an algorithm A(θ, d) → {0, 1}, where the binary output guesses if d was part of the unknown training data or not. It is commonly assumed that the attacker can access loss values from θ for
different prompts; such loss-based signals are crucial for membership checks, even under black-box conditions. For text, the notion of membership is not clear-cut: distinct strings can share the same semantics. Many MIAs therefore fail when they only test exact strings. Recent work reports near-random performance across models and domains (15). This suggests the need to incorporate semantics, i.e., to probe a text together with
semantic neighbours that preserve meaning under small, context-appropriate edits.
This thesis explores and strengthens such attacks and evaluates them with the standard metrics area under the ROC curve (AUC) and true positive rate at low false-positive rates (TPR@1%FPR). Building on the context-aware membership inference attack (CAMIA) which uses per-token loss sequences rather than a single average loss to construct signals for membership inference (11), the contributions of this thesis are: (i) a custom reimplementation of CAMIA, (ii) integrating a neighbourhood comparison signal that perturbs a text with its semantic neighbours (16), and (iii) novel signals designed to improve loss-informed neighbour generation. Experiments on Pythia-deduped
and GPT-Neo models across six subsets of The Pile (19) (streamed via the MIMIR
repository (15)) show that these semantics-aware extensions often increase true
positive rates at low false positive rates while keeping AUC stable. Overall, modest, loss-guided semantic edits make MIAs more effective for text under realistic black-box conditions.