Within-socket Myoelectric Prediction of Continuous Ankle Kinematics for Control of a Powered Transtibial Prosthesis

Objective. Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach. Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal \u27prediction\u27 interval between the EMG/kinematic input and the model\u27s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance. The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model\u27s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response

Volitional Control of Lower-limb Prosthesis with Vision-assisted Environmental Awareness

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether external emotional music stimuli could enhance the predictive capability of intention prediction methodologies. Application of advanced machine learning and signal processing techniques on pre-movement EEG resulted in an intention prediction system with low latency, high sensitivity and low false positive detection. Affective analysis of EEG suggested that happy music stimuli significantly (

Sensor-Based Adaptive Control and Optimization of Lower-Limb Prosthesis.

Recent developments in prosthetics have enabled the development of powered prosthetic ankles (PPA). The advent of such technologies drastically improved impaired gait by increasing balance and reducing metabolic energy consumption by providing net positive power. However, control challenges limit performance and feasibility of today’s devices. With addition of sensors and motors, PPA systems should continuously make control decisions and adapt the system by manipulating control parameters of the prostheses. There are multiple challenges in optimization and control of PPAs. A prominent challenge is the objective setup of the system and calibration parameters to fit each subject. Another is whether it is possible to detect changes in intention and terrain before prosthetic use and how the system should react and adapt to it. In the first part of this study, a model for energy expenditure was proposed using electromyogram (EMG) signals from the residual lower-limbs PPA users. The proposed model was optimized to minimize energy expenditure. Optimization was performed using a modified Nelder-Mead approach with a Latin Hypercube sampling. Results of the proposed method were compared to expert values and it was shown to be a feasible alternative for tuning in a shorter time. In the second part of the study, the control challenges regarding lack of adaptivity for PPAs was investigated. The current PPA system used is enhanced with impedance-controlled parameters that allow the system to provide different assistance. However, current systems are set to a fixed value and fail to acknowledge various terrain and intentions throughout the day. In this study, a pseudo-real-time adaptive control system was proposed to predict the changes in the gait and provide a smoother gait. The proposed control system used physiological, kinetic, and kinematic data and fused them to predict the change. The prediction was done using machine learning-based methods. Results of the study showed an accuracy of up to 89.7 percent for prediction of change for four different cases

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

The rate of development of robotic technologies has been meteoric, as a result of compounded advancements in hardware and software. Amongst these robotic technologies are active exoskeletons and orthoses, used in the assistive and rehabilitative fields. Artificial intelligence techniques are increasingly being utilised in gait analysis and prediction. This review paper systematically explores the current use of intelligent algorithms in gait analysis for robotic control, specifically the control of active lower limb exoskeletons and orthoses. Two databases, IEEE and Scopus, were screened for papers published between 1989 to May 2020. 41 papers met the eligibility criteria and were included in this review. 66.7% of the identified studies used classification models for the classification of gait phases and locomotion modes. Meanwhile, 33.3% implemented regression models for the estimation/prediction of kinematic parameters such as joint angles and trajectories, and kinetic parameters such as moments and torques. Deep learning algorithms have been deployed in ∼15% of the machine learning implementations. Other methodological parameters were reviewed, such as the sensor selection and the sample sizes used for training the models

Effectiveness of dual-task functional power training for preventing falls in older people: Study protocol for a cluster randomised controlled trial

Background: Falls are a major public health concern with at least one third of people aged 65 years and over falling at least once per year, and half of these will fall repeatedly, which can lead to injury, pain, loss of function and independence, reduced quality of life and even death. Although the causes of falls are varied and complex, the age-related loss in muscle power has emerged as a useful predictor of disability and falls in older people. In this population, the requirements to produce explosive and rapid movements often occurs whilst simultaneously performing other attention-demanding cognitive or motor tasks, such as walking while talking or carrying an object. The primary aim of this study is to determine whether dual-task functional power training (DT-FPT) can reduce the rate of falls in community-dwelling older people. Methods/Design: The study design is an 18-month cluster randomised controlled trial in which 280 adults aged =65 years residing in retirement villages, who are at increased risk of falling, will be randomly allocated to: 1) an exercise programme involving DT-FPT, or 2) a usual care control group. The intervention is divided into 3 distinct phases: 6 months of supervised DT-FPT, a 6-month 'step down' maintenance programme, and a 6-month follow-up. The primary outcome will be the number of falls after 6, 12 and 18 months. Secondary outcomes will include: lower extremity muscle power and strength, grip strength, functional assessments of gait, reaction time and dynamic balance under single- and dual-task conditions, activities of daily living, quality of life, cognitive function and falls-related self-efficacy. We will also evaluate the cost-effectiveness of the programme for preventing falls. Discussion: The study offers a novel approach that may guide the development and implementation of future community-based falls prevention programmes that specifically focus on optimising muscle power and dual-task performance to reduce falls risk under 'real life' conditions in older adults. In addition, the 'step down' programme will provide new information about the efficacy of a less intensive maintenance programme for reducing the risk of falls over an extended period. Trial registration: Australian New Zealand Clinical Trials Registry: ACTRN12613001161718. Date registered 23 October 2013

Development of an EEG-based recurrent neural network for online gait decoding

Recent neuroscientific literature has shown that the use of brain-controlled robotic exoskeletons in walking rehabilitation induces neuroplasticity modi- fications, possibly leading to a higher likelihood of recovery and maintenance of lost motor functions due to a neural lesion, with respect to traditional re- habilitation. However, the gait decoding from brain signals remains an open challenge. The aim of this work is to implement and validate a deep learning model for online gait decoding that exploits Electroencephalography (EEG) infor- mation to predict the intention of initiating a step, which could be used to trigger the assistance of a lower-limb exoskeleton. In particular, the model exploits a Gated Recurrent Units (GRU) deep neural network to handle the time-dependent features which were identified by analysing the neural cor- relates preceding the step onset (i.e., Movement-Related Cortical Potentials (MRCP)). The network was evaluated on a pre-recorded dataset of 11 healthy subjects walking on a treadmill. The network’s architecture (e.g., number of GRU units) was optimized through grid search. In addition, to deal with the data scarcity problem of neurophysiological applications, I proposed a data augmentation procedure to increase the dataset available to train the model of each subject. With the proposed approach, the model achieved an average accuracy in detecting the step onset of 89.7 ± 7.7% with just the 15% of the dataset for each subject (∼70 steps), and up to 97.8 ± 1.3% with the whole dataset (∼440 steps). This thesis support the use of a memory-based deep learning model to de- code walking activity from non-invasive brain recordings. In future works, this model will be exploited in real time as a more effective input for devices restoring locomotion in impaired people, such as robotic exoskeletons.Recent neuroscientific literature has shown that the use of brain-controlled robotic exoskeletons in walking rehabilitation induces neuroplasticity modi- fications, possibly leading to a higher likelihood of recovery and maintenance of lost motor functions due to a neural lesion, with respect to traditional re- habilitation. However, the gait decoding from brain signals remains an open challenge. The aim of this work is to implement and validate a deep learning model for online gait decoding that exploits Electroencephalography (EEG) infor- mation to predict the intention of initiating a step, which could be used to trigger the assistance of a lower-limb exoskeleton. In particular, the model exploits a Gated Recurrent Units (GRU) deep neural network to handle the time-dependent features which were identified by analysing the neural cor- relates preceding the step onset (i.e., Movement-Related Cortical Potentials (MRCP)). The network was evaluated on a pre-recorded dataset of 11 healthy subjects walking on a treadmill. The network’s architecture (e.g., number of GRU units) was optimized through grid search. In addition, to deal with the data scarcity problem of neurophysiological applications, I proposed a data augmentation procedure to increase the dataset available to train the model of each subject. With the proposed approach, the model achieved an average accuracy in detecting the step onset of 89.7 ± 7.7% with just the 15% of the dataset for each subject (∼70 steps), and up to 97.8 ± 1.3% with the whole dataset (∼440 steps). This thesis support the use of a memory-based deep learning model to de- code walking activity from non-invasive brain recordings. In future works, this model will be exploited in real time as a more effective input for devices restoring locomotion in impaired people, such as robotic exoskeletons

Decoding gait phases from neural activity in rat

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2017Introdução. A assistência médica prevista em casos de traumatismo na medula espinhal é escassa, o que em conjunto com a incapacidade de autorregeneração do sistema nervoso central, implica que a recuperação após trauma seja lenta e muitas vezes impossível. O conceito de uma interface cérebro-espinhal aparece quando exploramos o potencial da estimulação elétrica epidural como técnica de restauração da locomoção após trauma na medula espinhal. Esta técnica já provou ser eficaz em macacos, porém não em ratos. O modelo do rato é significativamente diferente, especialmente quando consideramos a complexidade da sua organização neuronal. Partindo desta problemática procurámos descobrir se é possível decodificar fases da marcha a partir da atividade neuronal em ratos. Este projeto foi desenvolvido durante um estágio de seis meses no laboratório de Gregoire Courtine, localizado no EPFL (École Polytechnique Fédérale de Lausanne), Suíça. Este laboratório especializa-se em neuro-reabilitação e neuro-regeneração. Ao longo desta dissertação será feita a análise e discussão deste projeto. Revisão da literatura. A marcha humana é produzida por uma série de contrações de músculos extensores e flexores a um ritmo predeterminado. Duas fases podem ser identificadas, uma fase de apoio seguida de uma fase de balanço. Os mecanismos que controlam a locomoção ainda não são completamente conhecidos, e a maioria da evidência encontrada surge de estudos realizados em modelos animais. No entanto, podem fornecer alguma orientação. Atualmente, sabe-se que não é necessário controlo supra-espinhal para produzir o ritmo básico da marcha, e que este padrão pode ser gerado por circuitos neuronais que existem na medula espinhal. Porém, várias estruturas do cérebro controlam e regulam as variantes da marcha em situações que envolvem uma marcha mais precisa e criteriosa. Os propriocetores musculares também têm um papel importante neste processo. Contudo considera-se que a marcha de um ser humano está mais dependente de um controlo cerebral. O córtex motor tem um papel de supervisão durante o decorrer da marcha e é a estrutura com o maior nível de abstração em termos da sua atividade elétrica, comparativamente a outras estruturas envolvidas na marcha. Apresenta muita atividade, especialmente quando um movimento requer a ativação de vários grupos musculares. Aquando de uma lesão espinhal, técnicas de reabilitação como a fisioterapia e a estimulação elétrica são utlizadas com algum grau de sucesso. Geralmente, o foco da reabilitação encontra-se em readquirir alguma qualidade de vida e destreza motora por parte do doente. No entanto nos casos em que a gravidade da lesão é tal que não existem células neuronais que mantenham qualquer ligação da espinhal medula as perspetivas de reabilitação tornam-se significativamente inferiores. Técnicas que potenciem a plasticidade neuronal e técnicas que viabilizem a regeneração neuronal devem ser então exploradas. A interface cérebro-espinhal utiliza a estimulação elétrica neuronal, controlando o seu ritmo, recorrendo a primitivas descodificadas de atividade neuronal que identificam momentos específicos do ciclo da marcha. Procuramos então obter uma prova de conceito, de que é possível obter variáveis discretas de locomoção a partir de atividade neuronal usando o modelo do rato. Métodos. A área que é conhecida por codificar informações sobre a locomoção no rato é o córtex sensoriomotor primário. Esta informação é transmitida através do caminho descendente do córtex sensoriomotor através da medula para os nervos eferentes que acionam os grupos musculares necessários na locomoção, garantindo a flexão e a extensão faseadas dos membros inferiores. Nos casos onde há uma lesão na medula espinhal e subsequente paralisia dos membros inferiores, a gravidade dos danos neuronais impedem a transmissão do sinal. O objetivo da interface cérebroespinal é capturar a atividade neuronal relacionada com a locomoção implantando uma matriz de microeléctrodos de 32 canais no córtex sensorimotor primário direito e usando métodos de classificação para prever momentos específicos do ciclo da marcha, que neste caso foram: o aplanamento e o impulso do pé. A nomenclatura usada para estes dois momentos foi de foot strike e foot off , respetivamente. Dois ratos fêmeas da raça Lewis designados por r263 e r328 receberam o implante cortical. Após o tempo de recuperação recomendado pós-cirurgia, prosseguimos com os ensaios, que consistiam na execução de aproximadamente um metro e meio de caminhada quadrupede. Um sistema de captura e análise de movimentos tridimensionais (Vicon Motion Systems®) foi utilizado para gravar as variáveis cinemáticas e o vídeo. No total, considerámos vinte e quatro sessões para r263 e trinta e uma sessão para r328. Após a análise das variáveis obtidas pelo sistema Vicon, extraímos o tempo real dos dois momentos do ciclo da marcha: foot strike e foot off. Os potenciais de campo locais (LFPs) obtidos durante os ensaios foram processados de modo a obter três componentes diferentes do signal: uma no domínio do tempo (LPC), e outras duas no domínio das frequências (TRFT-low and TRFT-high). Primeiramente, o sinal sofreu common average re-referencing e os ensaios e canais anormais foram removidos. Depois, para obtermos a LPC aplicamos um filtro Savitzky-Golay de segunda ordem. As outras duas componentes foram obtidas através da utilização de uma transformada de Fourier. A identificação da banda de frequência de TRFT-high e TRFT-low foi feita olhando para os valores de SNR ( Signal-to-noise ratio ). Para r263 TRFT-high estava entre os 3 e 15 Hz e TRFT-low entre os 39 e os 747 Hz. Para r328 TRFT-high estava entre os 3 e 21 Hz e TRFT-low entre os 105 e os 693 Hz. No final, para cada evento (foot strike, foot off e baseline) um total de 93 características foram extraídas sendo usadas para treinar um classificador de análise discriminante regularizado. Usando o método de validação cruzada, treinamos diferentes classificadores com diferentes combinações de parâmetros e selecionámos os valores de informação mútua como preditor do modelo que seria o ótimo. Toda a análise relativa à atividade neuronal foi feita com o auxílio do software Matlab®. Resultados & Discussão. Dos três componentes de sinal extraídos, TRFT-low demonstrou possuir a informação mais relevante em torno do momento de cada evento. O valor mais alto de informação mútua obtido para eventos relativos ao lado esquerdo da marcha foi de 0,617, considerando 1 o máximo. Relativamente aos eventos do lado direito, o desempenho do algoritmo foi 25-30% mais baixo, comparativamente. Facto este que pode ser justificado visto que o implante foi colocado no córtex sensório-motor direito. A continuação deste trabalho, requer mais ensaios e se possível num maior número de ratos. Conjuntamente, um algoritmo mais sofisticado e com uma maior precisão deve ser estudado. Também é importante continuar os esforços no sentido de perceber a dinâmica neuronal e de que maneira todos os sistemas se integram para garantir funções motoras num estado saudável de modo a otimizar a abordagem terapêutica em patologias que comprometem estes sistemas. Conclui-se dizendo que a ideia de uma interface cérebroespinal revela-se viável usando o modelo do rato, uma vez que é possível descodificar primitivas de marcha utilizando a atividade neuronal registada a partir do córtex sensório-motor. No entanto, isto foi apenas o primeiro passo no desenvolvimento de uma interface cérebroespinal completamente funcional.Introduction. Clinical assistance when it comes to nerve damage and spinal cord trauma falls short, and rehabilitation and recovery can sometimes be impossible due to the inability to self-regenerating. The brain spinal interface (BSI) is a concept that arises when exploring epidural electrical stimulation as a potential technique that is able to restore locomotion after a spinal cord injury. BSI’s in monkeys and humans have already been proven successful, however not in rats. The rat model is significantly different from the other ones, especially when it comes to its neural organization and complexity. For this reason we searched for proof that it is also possible to decode gait phases from neural activity in rat. This thesis was originated from the work done in a six month internship in Gregoire Courtine laboratory, based in Switzerland. Background. In rats the area that is known to encode information about movement is the primary sensorimotor cortex. This information is passed on through the descending neural pathway in the medulla and then on to the efferent nerves that trigger the necessary muscle groups that enforce motion and ensure time specific flexion and extension. In case of a spinal cord injury and subsequent lower limbs paralyses, the nerves are severed in such a way that this signal is lost. The BSI aims to capture gait related neural activity by implanting a 32-channel microelectrode array (Tucker-Davis Technologies (TDT), Alachua, FL, USA) in the right sensorimotor cortex and use classification methods to obtain quantitative prediction outputs. For the purposes of this thesis these outputs were the conditions of foot strike and foot off. Methods. We implanted two female Lewis rats designated by r263 and r328 and used a dedicated motion capture system (Vicon Motion Systems®) to record 3D kinematics and video. After sufficient recovery time after the surgery we proceeded to do the overground recordings. Each recording session consisted of one rat performing a full length runway walk walking quadrupely. We had 24 sessions for r263 and 31 for r328. From the Vicon files we extracted the real time of left foot off and left foot strike. The data sets containing the neural activity were pre-processed, and at the end we preserved 31 channels and extracted three different signal components (LPC, TRFT-low, TRFT-high). For each event (left foot off, left foot strike and baseline) we had a total of 93 extracted features that were used to train a regularized discriminant analysis classifier. Using cross-validation we trained different classifiers using different combinations of model parameters and choose the mutual information values to be our predictor for the optimum detection model. Results & Discussion. From the three extracted signal components, the TRFT-low showed the most information around the time of the event. The highest mutual information value found was of 0.617, considering that 1 was the highest possible number. We also built a decoder for predicting right side events, however it had a performance around 25-30 percent lower, comparatively to the left side prediction. This is justified by the fact that the implant was placed on the right sensorimotor cortex. The idea of a BSI, proves to be feasible on the rat model since it is possible to decode gait primitives using neural activity recorded from the sensorimotor cortex

Prediction of Freezing of Gait in Parkinson’s Disease using Wearables and Machine Learning

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-onesubject- out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness

Detecting intention to walk in stroke patients from pre-movement EEG correlates

Background: Most studies in the field of brain-computer interfacing (BCI) for lower limbs rehabilitation are carried out with healthy subjects, even though insights gained from healthy populations may not generalize to patients in need of a BCI. Methods: We investigate the ability of a BCI to detect the intention to walk in stroke patients from pre-movement EEG correlates. Moreover, we also investigated how the motivation of the patients to execute a task related to the rehabilitation therapy affects the BCI accuracy. Nine chronic stroke patients performed a self-initiated walking task during three sessions, with an intersession interval of one week. Results: Using a decoder that combines temporal and spectral sparse classifiers we detected pre-movement state with an accuracy of 64 % in a range between 18 % and 85.2 %, with the chance level at 4 %. Furthermore, we found a significantly strong positive correlation (r = 0.561, p = 0.048) between the motivation of the patients to perform the rehabilitation related task and the accuracy of the BCI detector of their intention to walk. Conclusions: We show that a detector based on temporal and spectral features can be used to classify pre-movement state in stroke patients. Additionally, we found that patients'' motivation to perform the task showed a strong correlation to the attained detection rate of their walking intention

Detection and Prediction of Freezing of Gait in Parkinson’s Disease using Wearable Sensors and Machine Learning

Freezing of gait (FOG), is a brief episodic absence of forward body progression despite the intention to walk. Appearing mostly in mid-late stage Parkinson’s disease (PD), freezing manifests as a sudden loss of lower-limb function, and is closely linked to falling, decreased functional mobility, and loss of independence. Wearable-sensor based devices can detect freezes already in progress, and intervene by delivering auditory, visual, or tactile stimuli called cues. Cueing has been shown to reduce FOG duration and allow walking to continue. However, FOG detection and cueing systems require data from the freeze episode itself and are thus unable to prevent freezing. Anticipating the FOG episode before onset and supplying a timely cue could prevent the freeze from occurring altogether. FOG has been predicted in offline analyses by training machine learning models to identify wearable-sensor signal patterns known to precede FOG. The most commonly used sensors for FOG detection and prediction are inertial measurement units (IMU) that include an accelerometer, gyroscope and sometimes magnetometer. Currently, the best FOG prediction systems use data collected from multiple sensors on various body locations to develop person-specific models. Multi-sensor systems are more complex and may be challenging to integrate into real-life assistive devices. The ultimate goal of FOG prediction systems is a user-friendly assistive device that can be used by anyone experiencing FOG. To achieve this goal, person-independent models with high FOG prediction performance and a minimal number of conveniently located sensors are needed. The objectives of this thesis were: to develop and evaluate FOG detection and prediction models using IMU and plantar pressure data; determine if event-based or period of gait disruption FOG definitions have better classification performance for FOG detection and prediction; and evaluate FOG prediction models that use a single unilateral plantar pressure insole sensor or bilateral sensors. In this thesis, IMU (accelerometer and gyroscope) and plantar pressure insole sensors were used to collect data from 11 people with FOG while they walked a freeze provoking path. A custom-made synchronization and labeling program was used synchronize the IMU and plantar pressure data and annotate FOG episodes. Data were divided into overlapping 1 s windows with 0.2 s shift between consecutive windows. Time domain, Fourier transform based, and wavelet transform based features were extracted from the data. A total of 861 features were extracted from each of the 71,000 data windows. To evaluate the effectiveness of FOG detection and prediction models using plantar pressure and IMU data features, three feature sets were compared: plantar pressure, IMU, and both plantar pressure and IMU features. Minimum-redundancy maximum-relevance (mRMR) and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or Non-FOG states, wherein the Total-FOG class included windows with data from 2 s before the FOG onset until the end of the FOG episode. The plantar-pressure-only model had the greatest sensitivity, and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, freeze windows, and transition windows between Pre-FOG and FOG). The best model, which used plantar pressure and IMU features, detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Models using both plantar pressure and IMU features performed better than models that used either sensor type alone. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect FOG detection and prediction model performance, especially with respect to multiple FOG in rapid succession. This research examined the effects of defining FOG either as a period of gait disruption (merging successive FOG), or based on an event (no merging), on FOG detection and prediction. Plantar pressure and lower limb acceleration data were used to extract a set of features and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging had little effect on FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession. Despite the known asymmetry of PD motor symptom manifestation, the difference between the more severely affected side (MSS) and less severely affected side (LSS) is rarely considered in FOG detection and prediction studies. The additional information provided by the MSS or LSS, if any, may be beneficial to FOG prediction models, especially if using a single sensor. To examine the effect of using data from the MSS, LSS, or both limbs, multiple FOG prediction models were trained and compared. Three datasets were created using plantar pressure data from the MSS, LSS, and both sides together. Feature selection was performed, and FOG prediction models were trained using the top 5, 10, 15, 20, 25 or 30 features for each dataset. The best models were the MSS model with 15 features, and the LSS and bilateral features with 5 features. The LSS model reached the highest sensitivity (79.5%) and identified the highest percentage of FOG episodes (94.9%). The MSS model achieved the highest specificity (84.9%) and the lowest false positive (FP) rate (2 FP/walking trial). Overall, the bilateral model was best. The bilateral model had 77.3% sensitivity, 82.9% specificity, and identified 94.3% of FOG episodes an average of 1.1 s before FOG onset. Compared to the bilateral model, the LSS model had a higher false positive rate; however, the bilateral and LSS models were similar in all other evaluation metrics. Therefore, using the LSS model instead of the bilateral model would produce similar FOG prediction performance at the cost of slightly more false positives. Given the advantages of single sensor systems, the increased FP rate may be acceptable. Therefore, a single plantar pressure sensor placed on the LSS could be used to develop a FOG prediction system and produce performance similar to a bilateral system