This report summarizes the existing literature on the use of technology for the assessment of freezing of gait (FOG) as well as the use of technology to provide insights into the mechanisms of FOG in people with Parkinson's disease. Specifically, this work was carried out for the 3rd International Workshop on Freezing of Gait in Jerusalem in 2023. This review focuses on the most used technologies to quantitatively assess FOG in a laboratory environment and describes the technologies that hold promise for assessing FOG in daily life. Examples of implementation of machine learning algorithms are provided as well as algorithmic biases. Lastly, a standardized assessment using inertial measurement units during a clinical protocol is proposed and a 5-year outlook is discussed. We anticipate this review will help move the field forward in the coming years.

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), characterized by an absence or reduction in forward movement of the legs despite the intention to walk. Detecting FOG during free-living conditions presents significant challenges, particularly when using only inertial measurement unit (IMU) data, as it must be distinguished from voluntary stopping events that also feature reduced forward movement. Influences from stress and anxiety, measurable through galvanic skin response (GSR) and electrocardiogram (ECG), may assist in distinguishing FOG from normal gait and stopping. However, no study has investigated the fusion of IMU, GSR, and ECG for FOG detection. Therefore, this study introduced two methods: a two-step approach that first identified reduced forward movement segments using a Transformer-based model with IMU data, followed by an XGBoost model classifying these segments as FOG or stopping using IMU, GSR, and ECG features; and an end-to-end approach employing a multi-stage temporal convolutional network to directly classify FOG and stopping segments from IMU, GSR, and ECG data. Results showed that the two-step approach with all data modalities achieved an average F1 score of 0.728 and F1@50 of 0.725, while the end-to-end approach scored 0.771 and 0.759, respectively. However, no significant difference was found compared to using only IMU data in both approaches (p-values: 0.466 to 0.887). In conclusion, adding physiological data did not provide a statistically significant benefit in distinguishing between FOG and stopping. The limitations may be specific to GSR and ECG data, and may not generalize to other physiological modalities.

Background: Home-based neurophysiological monitoring is improving the assessment and management of neurological conditions such as epilepsy. Technologies such as electroencephalography (EEG), electromyography (EMG), and accelerometry are increasingly integrated into wearable systems for at-home use. Due to an increasing amount of data from long-term monitoring, machine learning algorithms assist in automated data analysis. However, ensuring device accuracy, signal quality, and user compliance remains crucial for clinical useability. Objective: This chapter explores advances and challenges in at-home neurophysiological monitoring, with a primary focus on EEG systems and their applications. Content: The discussion highlights the technological advances and the challenges associated with at-home monitoring. The focus will be on EEG systems, as well as a discussion of EMG in epilepsy. Next, we will provide an overview of the clinical applications for home-based monitoring of epilepsy and sleep disorders. Lastly, we will briefly discuss emerging topics within home-based monitoring in movement disorders and neurodegenerative disorders. Conclusion: Future advancements are expected with new generations of wearable systems capable of providing long-term monitoring with minimal maintenance. Beyond epilepsy and sleep disorders, home-based technologies are also being investigated in other neurological diseases including movement disorders and neurodegenerative diseases showing the expanding scope of home-based technologies in neurology.

Assistive lower-limb exoskeletons (LLEs) have been recognized as promising tools for enhancing physical capacity in stroke survivors. Involving end-users in the early development stages is essential to ensure these technologies meet user needs. Co-design approaches, which actively engage end-users, support this goal. This study aims to (1) evaluate the impact of fatigue on daily living, (2) identify activities that could benefit from LLE assistance, (3) outline design and usability requirements for home-based LLEs, and (4) define physical parameters LLEs should monitor and assess. Discussions were structured using the PERCEPT co-design methodology and thematically analyzed. Four chronic stroke survivors participated in three focus group sessions, each lasting approximately 2 hours. Fatigue was identified as a significant factor in daily life, underscoring the importance of assistive technologies, such as LLEs, to help mitigate exhaustion. Participants recognized LLEs as valuable tools for enhancing physical performance, with benefits for muscle strength, balance, fatigue management, coordination, and general mobility. Design considerations included system modularity, battery efficiency, ease of donning and doffing, and practical needs for daily use. Our findings offer valuable insights into stroke survivors’ design and usability concerns regarding LLEs and provide a foundation for advancing the development and adoption of new assistive technologies.

Continual Spatial-Temporal Graph Convolutional Network (CoST-GCN) is a continual inference optimization of ST-GCN, an established graph-based action classification method. It removes redundant computations in the ST-GCN classifier when applied as a sliding window over a continual stream of data for per-frame predictions. Despite the improvement of CoST-GCN we can only achieve a throughput of 5 fps on a representative edge platform (Raspberry Pi 4). We propose a hardware-driven optimization, termed RT-ST-GCN, which scales down the computational bottleneck of ST-GCN to achieve realtime predictions up to 50 fps. We study and compare the performance of our lightweight model against (Co)ST-GCN on the PKU-MMD continual action dataset. Despite an expected drop in framewise performance metrics, our model shows similar or better performance on key segmental metrics, a constant latency of 20 ms for any temporal kernel size and 3x decrease in memory usage.

To gain insights into the impact of upper limb (UL) dysfunctions after breast cancer treatment, this study aimed to develop a temporal convolutional neural network (TCN) to detect functional daily UL use in breast cancer survivors using data from a wrist-worn accelerometer. A pre-existing dataset of 10 breast cancer survivors was used that contained raw 3-axis acceleration data and simultaneously recorded video data, captured during four daily life activities. The input of our TCN consists of a 3-axis acceleration sequence with a receptive field of 243 samples. The 4 ResNet TCN blocks perform dilated temporal convolutions with a kernel of size 3 and a dilation rate that increases by a factor of 3 after each iteration. Outcomes of interest were functional UL use (minutes) and percentage UL use. We found strong agreement between the video and predicted data for functional UL use (ICC = 0.975) and moderately strong agreement for %UL use (ICC = 0.794). The TCN model overestimated the functional UL use by 0.71 min and 3.06%. Model performance showed good accuracy, f1, and AUPRC scores (0.875, 0.909, 0.954, respectively). In conclusion, using wrist-worn accelerometer data, the TCN model effectively identified functional UL use in daily life among breast cancer survivors.

— Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson’s disease (PD). Although described as a single phenomenon, FOG is heterogeneous and can express as different manifestations, such as trembling in place or complete akinesia. We aimed to analyze the efficacy of deep learning (DL) trained on inertial measurement unit data to classify FOG into both manifestations. We adapted and compared four state-of-the-art FOG detection algorithms for this task and investigated the advantages of incorporating a refinement model to address oversegmentation errors. We evaluated the model’s performance in distinguishing between trembling and akinesia, as well as other forms of movement cessation (e.g., stopping and sitting), against gold-standard video annotations. Experiments were conducted on a dataset of eighteen PD patients completing a FOG-provoking protocol in a gait laboratory. Results showed our model achieved an F1 score of 0.78 and segment F1@50 of 0.75 in detecting FOG manifestations. Assessment of FOG severity was strong for trembling (ICC=0.86, [0.66,0.95]) and moderately strong for akinesia (ICC=0.78, [0.51,0.91]). Importantly, our model successfully differentiated FOG from other forms of movement cessation during 360-degree turning-in-place tasks. In conclusion, our study demonstrates that DL can accurately assess different types of FOG manifestations, warranting further investigation in larger and more diverse verification cohorts.

Purpose Physical activity through exercise helps to delay, prevent, or reverse functional decline in older adults (Bean et al., 2004). Recently, various technologies, including assistive robot coaches, have been developed to engage older adults in physical activities (Avioz-Sarig et al., 2021). Nevertheless, there is a lack of understanding regarding the specific needs, preferences, and potential barriers faced by older adults and their caregivers when interacting with this technology (Fasola and Mataric, 2012). This study is part of the interdisciplinary AI@WZC project, aimed at developing an AI-driven robotic exercise coach for older adults living in a residential care home. We aimed to acquire insights from residents and their caregivers to inform the design of this robotic coach. Method We organized two semi-structured focus groups with respectively five and four care home residents (age: 70–90 years old; gender: session 1 with three females and two males, session 2 with two females and two males; MMSE: 18-30) and two caregivers (one physical therapist, one nurse), adhering to the participatory design principles of the PERCEPT methodology (Bourazeri and Stumpf, 2018). During the first workshop, we gained insights into their technology use, physical challenges, and daily activities. This discussion aided the participants to co-create personas for a care home resident, a physical therapist, and a nurse. In the second workshop, these personas served as a basis to facilitate further discussions. Here, we asked participants to rank their preferred physical exercises related to training specific body parts (e.g., ankle, shoulder, neck), and inquired about their preferences related to functionalities the robot should have, with emphasis on the information that the robot should provide to them through its interface. Results and Discussion Participants have varying physical abilities and preferences regarding physical exercises they engage in. For example, care home residents use different mobility aids (e.g., walker, wheelchair), which impacts which exercises are suitable and safe. Therefore, it is necessary that caregivers can individually assess care home residents’ training progress with the robot, and make adjustments to ensure the exercises fit their individual needs. Flexibility is also important regarding the planning of the robotic coach’ visits. For example, the residents often engage in group or individual exercise sessions with a physical therapist. As such, participants voiced that training with the robot should not be scheduled too close to a session with a physical therapist. Furthermore, participants want to know in advance when an exercise session with the robot is planned, and want to have the freedom to cancel or reschedule such a session when it does not suit them, e.g., in case of unexpected family visitors, appointment with their doctor, or other activities. The design needs to take into account their limited experience with technology, while avoiding burdening caregivers with additional work load to manage the robot’s schedule. Regarding the interface of the robot, participants indicated that feedback primarily needs to be straightforward and functional, e.g., the robot needs to inform them about their progress during the training, letting them know how long it takes before they completed the exercise. Participants also expressed the wish not be interrupted during their training with the robot, meaning that non-functional distractions (e.g., visual embellishments, abundant feedback or encouragement) should be kept to a minimum. Summarizing these results, we identified two key user requirements based on the data collected during the two sessions. (1) The robotic coach needs to serve as an assessment tool, allowing caregivers to monitor functional decline and ensure the safety of exercise regimens. (2) It needs to enable personalization of exercise regimens according to individual preferences and physical limitations of care home residents, therefore allowing them to maintain agency regarding their exercise routines. At the same time, interaction between the robot and residents should be minimal, with a focus on functional feedback during exercises. In conclusion, this approach and the findings from this study can inspire and guide future researchers in developing assistive physical exercise technologies for use in residential care homes.