Hands-on workshops or interactive activities will allow students to understand the basic principles of the technologies, plan clinical interventions and experiments and use the devices and tools in real applications.

Students will complete two tracks of workshops (one in the early afternoon and one during the late afternoon) on June 17 (Monday), June 18 (Tuesday), and June 20 (Thursday), based on selected preference and workshop availability. The content of each workshop will span all three days, so it is necessary to attend the three days to make the most of the workshop.

July 26, 2024: SSNR2024 has now concluded, so we have closed the SSNR2024 Workshop Pre-Work page and accounts have been deactivated.

Updated: May 29, 2024 – The SSNR2024 Student Workshop Assignments have been posted! Please check this carefully, and make sure that you check out the associated Pre-Work page info so you are prepared for the workshop content. Some workshops require you to download software, scripts, and data.

Workshop Track A (early afternoon): WS3, WS2, WS5, WS8
Workshop Track B (late afternoon): WS1, WS4, WS6

Due to low student interest and space considerations, WS7 has been changed to a talk on Jun/19/Wed morning and WS9 has been cancelled.

WS1 – High-density electromyography (HD-EMG) to investigate motor control and coordination
In this workshop, we will introduce the technique of using high-density surface electromyography (HD-EMG) to record and estimate the neural signal sent from muscles from the spinal motor neurons. Specifically, we will present methods for processing neural signals and the applications for understanding impaired motor control. Furthermore, we will provide clinical perspectives on the use of HD-EMG on pathological populations and challenges to clinical implementation. Students will be engaged in recording and processing HD-EMG signals using instruments and tools provided by the organizers. They will learn the techniques for HD-EMG recording, decomposition of the HD-EMG signals, editing of the motor neuron firings, and post-processing of motor neuron firings. Additionally, we will introduce and demo a novel real time paradigm of motor unit firing properties.
Hosted by: Shirley Ryan AbilityLab (SRAlab) – Dr. José L. Pons’ lab: Jackson Levine, Xin Yu
Relevant populations: stroke
Techniques involved: HD-EMG

WS1 Day 1: HD-EMG Recording Training
– What is HD-EMG and why do we use it?
– How to record HD-EMG?
– Hands-on HD-EMG recording
– Applications of HDEMG
– Difficulties/ considerations of HD-EMG with a clinical population

WS1 Day 2: HD-EMG Processing
– What can we do with HD-EMG?
– HD-EMG Motor Unit Editing
– Motor Unit Processing
– What could motor unit outcomes mean to clinicians?

WS1 Day 3: Realtime Motor Unit Biofeedback with ISpin
– Theory of realtime decomposition
– How to use the ISpin software
– Hands-on use of the ISpin software
– Discussion of papers using software and clinical applications

WS2 – Robotic interventions and assessments for sensorimotor impairments
In this workshop, students will learn about using robotic devices to assess and treat sensorimotor deficits in individuals with neurological conditions. Hands-on activities include designing and using virtual haptic environments (e.g., transparent control, assistance/resistance) for 1 and 2-DoF robots which can be implemented in assessments for clinical populations (e.g., motor control, range of motion). Presentations will cover the implementation of robotics in clinical settings as well as examples of sensorimotor impairments associated with stroke and spinal cord injury which can be targeted from the perspective of a physical therapist.
Hosted by: Shirley Ryan AbilityLab (SRAlab) – Dr. José L. Pons’ lab: Matthew Short, Shoshana Clark; TUDelft – Dr. Laura Marchal-Crespo, Irene Beck, Alberto Garzas Villar
Contributions by: Dr. Trent Maruyama
Relevant populations: stroke, spinal cord injury
Techniques involved: MATLAB, Python

WS2 Day 1: Implementation of robotics in clinical settings
– What are the challenges associated with implementing robots in clinical settings?
– Demonstration of Fourier Intelligence devices for upper- and lower-limb rehabilitation

WS2 Day 2: Design of virtual environments for upper-limb haptics
– Overview of haptic training methods
– Hands-on programming of haptic devices for wrist/hand exercises

WS2 Day 3: Towards human-robot-human interactions for rehabilitation
– What is group therapy in the context of physical rehabilitation?
– Demonstration of robot-mediated physical interaction between pairs of individuals

WS3 – Activity-dependent peripheral electrical stimulation for neuromodulation
This workshop provides a comprehensive understanding of the principles and techniques for using electrical stimulation for inducing neuromodulation via transcutaneous electrical nerve stimulation (TENS), e.g., sensory-specific peripheral stimulation (sPES). It will also introduce students to techniques that can be used to evaluate neuromodulation at the corticospinal (Transcranial magnetic stimulation) and spinal (H-reflex conditioning) levels. Students will learn about underlying mechanisms, physiological and anatomical principles, different parameters that can be adjusted for changing the neuromodulation effect using electrical stimulation (e.g., frequency, intensity, and duration), and how these parameters can be tailored to individual patient needs. Throughout the workshop, participants will have the opportunity to ask questions and practice the techniques (hands-on) to gain a deeper understanding of the techniques used for inducing and assessing neuromodulation.
Hosted by: Shirley Ryan AbilityLab (SRAlab) – Dr. José L. Pons’ lab: Nish Mohith Kurukuti, Hamidollah Hassanlouei, Xin Yu
Techniques involved: TENS, TMS, H-reflex

WS3 Day 1: Introduction to sPES for neuromodulation
– Neurophysiological considerations of sPES and stimulation strategies for neuromodulation
– Hands-on testing of various sPES stimulation strategies

WS3 Day 2: Transcranial Magnetic Stimulation for assessing corticospinal excitability
– Introduction to TMS and assessment of corticospinal excitability
– Hands-on with TMS for assessing corticospinal excitability

WS3 Day 3: Spinal reflex paradigm for assessing spinal excitability
– Introduction to spinal reflex (H-reflex) and stimulation paradigms to assess spinal excitability
– Hands-on with quantifying H-reflex for assessing spinal excitability

WS4 – Control and personalization of lower limb wearable robots
We will present how wearable robotics, and specifically exoskeletons, are typically controlled to provide assistance in various activities of daily living (such as overground walking, ramps, stairs, sit-to-stand). In particular, a state machine-based controller will be introduced and students will tune a state machine. We will also present how machine learning (ML) techniques are changing wearable robots control, and students will use data, and a neural network to complete a ML task for wearable robotic locomotion control, and tested in realtime.
Hosted by: Shirley Ryan AbilityLab (SRAlab) – Dr. José L. Pons’ lab: Lorenzo Vianello;  Hospital Los Madroños – Alberto Cantón Gonzalez
Tentative contributions by: Technaid
Relevant populations: stroke, spinal cord injury, amputation
Techniques involved: MATLAB, ROS C++, Gazebo, Python

WS4 Day 1: Finite State Machine Tuning for the X2 exoskeleton
– Introduction to controllers designed to provide assistance to lower-limb exoskeletons in everyday life
– Presentation of the X2 lower limb exoskeleton
– Introduction of planned activities, review of FSM impedance control, and description of control task
– Hands-on tuning of parameters in a simulation
– Hands-on testing and tuning impedance controller on the X2 exoskeleton

WS4 Day 2: Machine Learning for Wearable Robots Control
– Introduction to ML techniques for the control of wearable robots (prosthetic and exoskeletons) designed to provide assistance to lower-limb exoskeletons in everyday life
– Review of ML techniques for regression of locomotion characteristics (gait phase, locomotion mode)
– Hands-on tuning of parameters in a simulation
– Hands-on testing and tuning impedance controller on the X2 exoskeleton

WS4 Day 3: Technaid
To be announced

WS5 – Dynamic Simulation of Assistive Robotics and Human Motion
In simulated environments we can design, test, and evaluate novel concepts for robotic devices and their control in an accessible and iterative way. This workshop will introduce a physics engine and will cover the basics of constructing virtual scenes of human motion and robotic devices. Through a series of hands-on programming exercises participants will construct and control virtual systems in both upper and lower limb settings. Novel methods for synthesizing biosignals such as motion data or EMG using neuromechanical models will be introduced. Note: The real-time myoelectric control of the virtual hand developed in this workshop can be trialed in WS6 “Human-Machine Interfacing and Mechatronics for Myoelectric Prosthetics”.
Hosted by: Imperial College London (ICL) – Dr. Dario Farina’s lab: Balint Hodossy, Arnault Caillet
Relevant populations: Amputees, stroke
Techniques involved: Biomechanics, Python, Modelling, Signal processing, Control

WS6 – Human-Machine Interfacing and Mechatronics for Myoelectric Prosthetics
This workshop examines the design, assembly and control of wearable robotic devices through the example of a prosthetic hand.
Hosted by: Imperial College London (ICL) – Dr. Dario Farina’s lab: Patrick Sagastegui, Jumpei Kashiwakura
Relevant populations: Transradial amputees, prosthesis and orthosis users
Techniques involved: Mechatronics, MATLAB, Signal processing

WS6 Day 1: Neural interfacing
– Myoelectric pipeline
– Interfacing with the nervous system
– Control approaches in prosthetics
– Project structure and task overview
– Demonstration

WS6 Day 2: sEMG Processing
– Feature extraction
– Implementation of feature extraction
– Data-driven control approach
– Implementation of algorithm control
– Testing algorithm
– Apply feature extraction and control to detect movement intention on real time

WS6 Day 3: Prosthetic interfacing
– Introduction of mechatronics of upper limb prostheses
– Hands-on assembling of myoelectric upper limb prostheses
– Controlling the assembled myoelectric upper limb prosthetics (from WS6 Day 1) by using the developed control algorithm (from WS6 Day 2)

WS7 – 4D scanning tools for clinical applications

Due to space considerations and student sign-ups, this workshop has been converted into a morning talk.

WS8 – Machine Learning Processing for Wearable Data in Healthcare: Classification and Regression Cases in Rehabilitation Event Detection
This practical workshop presents a comprehensive overview of the usage of machine learning for processing wearable data (periodic and non-periodic time series) in healthcare settings, with application in the neurorehabilitation process. The course covers classification and regression methods to detect health-associated events, emphasizing the classification of Activities of Daily Living (ADL) using unprocessed kinematic and physiological signal data. Students will engage in neural networks, feature engineering, and data imputation and have the option to collect additional data using proprietary sensors to enhance their analytical pipelines. The workshop introduces the concept of variational autoencoders for condensing data into a conceptually informed latent space. This method is designed to isolate attributes into vectors specific to different health condition tracking from the cardiovascular effort to integrated neurorehabilitation recovery within an attribute graph. These methods aim to achieve decision-making support for physicians. The course will provide hands-on experience in evaluating this model using multimodal wearables and health records, focusing on detecting ADLs and regression of cardiovascular effort from existing datasets. This practical application will allow students to explore machine learning’s potential to improve clinical outcomes through precision neurorehabilitation.
Hosted by: SCAI Lab – Dr. Diego Paez and Mehdi Ejtehadi
Techniques involved: Machine learning

WS8 Day 1: Introduction to ML-driven Feature Extractions in Healthcare Data
– General introduction to boosting algorithms and metrics of assessments for ML and DL models
– In-depth tutorial on time-series data segmentation, pre-processing, feature extraction, imputation and labelling evaluation
– Hands-on work with Wearables Sensors and Data pre-processing

WS8 Day 2: Evaluation Metrics for Acceptable Machine Learning
– Introduction to data quality assessment and model evaluation metrics with a focus on explainability, robustness and generalization
– Hands-on work with model design and training

WS8 Day 3: Learning Evaluation and Proper Metrics
– Boosting ML vs Neural Networks in Non-periodic Time Series Applications
– Overview of disentangled variational autoencoders, and graph representation learning as a method for explainable learning in AI
– Hands-on work with model evaluation, fine-tuning and results assessment
– Presentations and Evaluation of final algorithms by the students

WS9 – Implantable permanent magnets to interface humans with assistive devices
Due to space considerations and student sign-ups, this workshop has been cancelled.

Updated May 27, 2024.