While current biomechanics research based on normal models and assumptions of normalcy has substantial merit, it fails to reliably describe individuals with impairments. Spinal cord injury (SCI), whether traumatic or nontraumatic, can partially or completely damage sensorimotor pathways, leading to heterogeneous gait abnormalities. A substantial knowledge gap exists regarding biomechanical and neurological movement strategies in this population due to complex, interacting factors including age, weight, time since injury, pain, sensorimotor impairment, and spasticity. The ASIA Impairment Scale, while recommended for classifying injury severity, was not designed to characterize individual ambulatory capacity. Other standardized assessments based on subjective ratings or timing/distance measures have limited ability to characterize functional capacity in this population comprehensively.

This thesis therefore aims to create computational frameworks for studying walking strategies in individuals with SCI, particularly incomplete SCI (iSCI), through two complementary approaches: developing machine learning algorithms that link individual characteristics to gait outcomes, and individualizing objective functions and constraints in predictive simulations using neuromusculoskeletal modeling.

Study I proposed and evaluated a framework applying Gaussian Process Regression and SHapley Additive exPlanations (SHAP) to quantify how neurological impairments and other demographic and anthropometric factors contribute to walking speed and net Oxygen cost during a six-minute walk test. Individual SHAP analyses quantified how these factors influenced walking performance for each participant, informing personalized rehabilitation targeting areas with the most potential for improvement.

Study II stratified gait heterogeneity in individuals with iSCI by deriving clusters with similar gait patterns without a priori parameter identification and assessed clinical correlations within the derived clusters. Six distinct gait clusters were identified and characterized among 280 iSCI gait cycles, informing more individualized rehabilitation.

Study III characterized margin of stability, temporospatial parameters, and joint mechanics in four iSCI subgroups from Study II compared to participants without disability, identifying how gait adaptations evolve as muscle weakness affects major muscle groups. Gait patterns remained normal with isolated mild plantarflexor weakness but deteriorated with combined hip muscle weakness and severe plantarflexor weakness.

Study IV developed a bilevel optimization framework using Bayesian optimization to automatically identify optimal objective weights for predictive gait simulations in individuals with iSCI. Tested on one female participant with asymmetric muscle weakness, the framework successfully automated weight identification in 9-12 days and demonstrated that simulations with optimized weights outperformed literature-based reference weights for predicting kinematics, kinetics, and ground reaction forces, showing promise for systematically exploring personalized compensatory gait strategies with predictive simulations.

These findings demonstrate the potential of advanced data-driven and simulation techniques to address gait complexity in individuals with SCI, with broader applicability to other clinical populations.

Även om nuvarande biomekanikforskning baserad på normala modeller och antaganden om normalitet har betydande förtjänster, misslyckas den med att på ett tillförlitligt sätt beskriva individer med funktionsnedsättningar under normativ funktion. Ryggmärgsskada (SCI), vare sig traumatisk eller icke-traumatisk (nontraumatisk), kan delvis eller fullständigt skada sensomotoriska banor, vilket leder till heterogena gångavvikelser. Det finns en betydande kunskapslucka gällande biomekaniska och neurologiska rörelsestrategier i denna population på grund av komplexa, interagerande faktorer inklusive ålder, vikt, tid sedan skada, smärta, sensomotorisk funktionsnedsättning och spasticitet. ASIA Impairment Scale, även om den rekommenderas för klassificering av skadans svårighetsgrad, utformades inte för att karakterisera individuell ambulatorisk kapacitet. Andra standardiserade bedömningar baserade på subjektiva skattningar eller tid-/distansmått har likaså begränsad förmåga att på ett heltäckande sätt karaktärisera funktionell kapacitet i denna population.

Den här avhandlingen syftar därför till att skapa sådana beräkningsmässiga ramverk för att studera gångstrategier hos individer med SCI, i synnerhet inkomplett SCI (iSCI), genom två kompletterande tillvägagångssätt: att utveckla maskininlärningsalgoritmer som kopplar individuella egenskaper till gångutfall, och att individualisera målfunktioner och optimeringsvillkor i prediktiva simuleringar genom neuromuskuloskeletal modellering.

Studie I föreslog och utvärderade ett ramverk som tillämpade Gaussisk Processregression och SHapley Additive exPlanations (SHAP) för att kvantifiera hur neurologiska funktionsnedsättningar och andra demografiska och antropometriska faktorer bidrar till gånghastighet och netto syrgaskostnad under ett sex-minuters gångtest. Individuella SHAP-analyser kvantifierade hur dessa faktorer påverkade gångprestationen för varje deltagare, vilket informerade personlig rehabilitering riktad mot områden med störst potential för förbättring.

Studie II stratifierade gångheterogenitet hos individer med iSCI genom att härleda kluster med liknande gångmönster utan a priori parameteridentifiering och bedömde kliniska korrelationer inom de härledda klustren. Sex distinkta gångkluster identifierades och karakteriserades bland 280 iSCI-gångcykler, vilket representerar ett första steg mot individualiserade rehabiliteringsprogram.

Studie III karakteriserade stabilitetsmarginal, temporospatiala parametrar och ledmekanik i fyra iSCI-undergrupper från Studie II jämfört med en kontrollgrupp, och identifierade hur gånganpassningar utvecklas när muskelsvaghet progressivt påverkar större muskelgrupper. Gångmönstren förblev normala vid isolerad mild plantarflexorsvaghet men försämrades med kombinerad höftmuskelsvaghet och allvarlig plantarflexorsvaghet.

Studie IV utvecklade ett optimeringsramverk som fungerar på två nivåer. Ramverket använder Bayesiansk optimering för att automatiskt  identifiera optimala objektivvikter för prediktiva gångsimuleringar hos individer med iSCI. Vid testning på en kvinnlig deltagare med asymmetrisk muskelsvaghet lyckades ramverket automatisera viktidentifiering på 9-12 dagar och visade att optimala vikter presterade bättre än standardinställningar från litteraturen för att förutsäga kinematik, kinetik och markreaktionskrafter, vilket visar lovande potential för att systematiskt utforska personliga kompensatoriska gångstrategier med prediktiva simuleringar.

Dessa fynd demonstrerar potentialen hos avancerade datadrivna och simuleringsbaserade tekniker för att adressera gångkomplexitet hos individer med SCI, med bredare tillämplighet på andra kliniska populationer.

Background: Incomplete spinal cord injury (iSCI) often causes heterogeneous locomotion dysfunctions, depending on remaining sensorimotor function. Clinical tests and traditional gait analysis have limited ability to quantify the diversity of gait impairments. Unsupervised learning techniques can objectively identify common gait patterns among the overall heterogeneity. Explainable artificial intelligence approaches, when combined with machine learning models, can reveal important features often missed by traditional gait analyses. This study presents a framework to characterize gait heterogeneity among persons with iSCI based on several data-driven methods. We aimed to stratify overall gait heterogeneity by deriving clusters with similarities without a priori identification of parameters, and to assess possible clinical correlations in the derived clusters. Methods: A cohort of 28 adults with iSCI and control group of 21 non-disabled adults were recruited. The iSCI group underwent a standard physical assessment of overall mobility, lower extremity strength, and spasticity. Both groups underwent instrumented 3D gait analysis, walking at self-selected pace. Distinct iSCI gait pattern subgroups were identified with dependent dynamic time warping and hierarchical agglomerative clustering. Distribution of clinical descriptives and outcome measures among and between groups were evaluated. Gait predictors that distinguish each cluster from control gait were identified with a random forest classifier and explainable AI. Results: Six distinct gait clusters were identified among the 280 iSCI gait cycles. Clusters with relatively low walking speed exhibited shorter step lengths and less ankle plantarflexion in pre-swing than controls. Gait patterns and walking performance in clusters with high walking speed were relatively similar to controls. Overall muscle strength, walking independence, walking speed, step length, step width, sex distribution, and types of walking aids significantly differed between all six clusters. Ankle plantarflexion angle in pre-swing correlated strongly with walking speed and step length. Conclusion: Through a series of advanced data-driven approaches, common gait patterns can be objectively identified and comprehensively characterized within a heterogeneous iSCI population. This work represents an initial step in developing individualized rehabilitation programs for persons with iSCI.

Spinal cord injury (SCI) can impair sensorimotor pathways and reduce walking ability. The effects of sensorimotor impairments are complex, as they interact with other factors such as age, pain, injury level, and injury severity. Traditional regression analyses have been used to describe relationships, but they frequently assume linearity. Explainable AI methods such as SHapley Additive ex-Planations (SHAP), based on cooperative game theory, can reveal feature importance globally and locally. Gaussian Process Regression (GPR) can handle limited datasets, a common challenge in observational clinical studies with small sample sizes. In this study, we proposed and evaluated a framework applying GPR and SHAP to quantify how neurological impairments and other factors, including muscle strength, sensory function, age, pain, spasticity, etc., contribute to walking performance, specifically walking speed and net oxygen cost during a six-minute walk test. This approach estimates each factor’s contribution both on a group level and for each individual. Thirty four adults with SCI underwent a clinical assessment and the six-minute walk test with preferred walking aids if relevant. Muscle strength was the most influential factor in both walking speed and net oxygen cost. Male sex, lower age, and less pain were associated with increased walking speed. More pain, higher body mass index, and higher sensory score were associated with lower net oxygen cost. Spasticity, injury level and sensory injury levels had relatively small influence on either outcome measure. Individual SHAP analyses quantified how neurological factors influenced walking performance for each participant. We demonstrate how nonparametric regression and explainable AI provide insights into the complex neurological factors affecting walking ability in persons with SCI.

This is a protocol for comprehensive analysis of gait and affecting factors in individuals with incomplete paraplegia due to spinal cord injury (SCI). A SCI is a devastating event affecting both sensory and motor functions. Due to better care, the SCI population is changing, with a greater proportion retaining impaired ambulatory function. Optimizing ambulatory function after SCI remains challenging. To investigate factors influencing optimal ambulation, a multi-professional research project was grounded with expertise from clinical rehabilitation, neurophysiology, and biomechanical engineering from Karolinska Institutet, the Spinalis Unit at Aleris Rehab Station (Sweden's largest center for specialized neurorehabilitation), and the Promobilia MoveAbility Lab at KTH Royal Institute of Technology. Ambulatory adults with paraplegia will be consecutively invited to participate. Muscle strength, sensitivity, and spasticity will be assessed, and energy expenditure, 3D movements, and muscle function (EMG) during gait and submaximal contractions will be analyzed. Innovative computational modeling and data-driven analyses will be performed, including the identification of clusters of similar movement patterns among the heterogeneous population and analyses that study the link between complex sensorimotor function and movement performance. These results may help optimize ambulatory function for persons with SCI and decrease the risk of secondary conditions during gait with a life-long perspective.

Bioelectric signal classification is a flourishing area of biomedical research, however conducting this research in a clinical setting can be difficult to achieve. The lack of inexpensive acquisition hardware can limit researchers from collecting and working with real-time data. Furthermore, hardware requiring direct connection to a computer can impose restrictions on typically mobile clinical settings for data collection. Here, we present an open-source ADS1299-based bioelectric signal acquisition system with wireless capability suitable for mobile data collection in clinical settings. This system is based on the ADS_BP and BioPatRec, both open-source bioelectric signal acquisition hardware and MATLAB-based pattern recognition software, respectively. We provide 3D-printable housing enabling the hardware to be worn by users during experiments and demonstrate the suitability of this platform for real-time signal acquisition and classification. In conjunction, these developments provide a unified hardware-software platform for a cost of around 150 USD. This device can enable researchers and clinicians to record bioelectric signals from non-disabled or motor-impaired individuals in laboratory or clinical settings, and to perform offline or real-time intent classification for the control of robotic and virtual devices.

Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.

Spinal cord injury, whether traumatic or nontraumatic, can partially or completely damage sensorimotor pathways between the brain and the body, leading to heterogeneous gait abnormalities. Mobility impairments also depend on other factors such as age, weight, time since injury, pain, and walking aids used. The ASIA Impairment Scale is recommended to classify injury severity, but is not designed to characterize individual ambulatory capacity. Other standardized tests based on subjective or timing/distance assessments also have only limited ability to determine an individual's capacity. Data-driven techniques have demonstrated effectiveness in analysing complexity in many domains and may provide additional perspectives on the complexity of gait performance in persons with spinal cord injury. The studies in this thesis aimed to address the complexity of gait and functional abilities after spinal cord injury using data-driven approaches.

The aim of the first manuscript was to characterize the heterogeneous gait patterns in persons with incomplete spinal cord injury. Dissimilarities among gait patterns in the study population were quantified with multivariate dynamic time warping. Gait patterns were classified into six distinct clusters using hierarchical agglomerative clustering. Through random forest classifiers with explainable AI, peak ankle plantarflexion during swing was identified as the feature that most often distinguished most clusters from the controls. By combining clinical evaluation with the proposed methods, it was possible to provide comprehensive analyses of the six gait clusters.    

The aim of the second manuscript was to quantify sensorimotor effects on walking performance in persons with spinal cord injury. The relationships between 11 input features and 2 walking outcome measures - distance walked in 6 minutes and net energy cost of transport - were captured using 2 Gaussian process regression models. Explainable AI revealed the importance of muscle strength on both outcome measures. Use of walking aids also influenced distance walked, and  cardiovascular capacity influenced energy cost. Analyses for each person also gave useful insights into individual performance.    

The findings from these studies demonstrate the large potential of advanced machine learning and explainable AI to address the complexity of gait function in persons with spinal cord injury.

Skador på ryggmärgen, oavsett om de är traumatiska eller icke-traumatiska, kan helt eller delvis skada sensoriska och motoriska banor mellan hjärnan och kroppen, vilket påverkar gången i varierande grad. Rörelsenedsättningen beror också på andra faktorer såsom ålder, vikt, tid sedan skadan uppstod, smärta och gånghjälpmedel. ASIA-skalan används för att klassificera ryggmärgsskadans svårighetsgrad, men är inte utformad för att karaktärisera individens gångförmåga. Andra standardiserade tester baserade på subjektiva eller tids och avståndsbedömningar har också begränsad möjlighet att beskriva individuell kapacitet. Datadrivna metoder är kraftfulla och kan ge ytterligare perspektiv på gångens komplexitet och prestation. Studierna i denna avhandling syftar till att analysera komplexa relationer mellan gång, motoriska samt sensoriska funktion efter ryggmärgsskada med hjälp av datadrivna metoder.

Syftet med den första studien är att karaktärisera de heterogena gångmönster hos personer med inkomplett ryggmärgsskada. Multivariat dynamisk tidsförvrägning (eng: Multivariate dynamic time warping) användes för att kvantifiera gångskillnader i studiepopulationen. Hierarkisk agglomerativ klusteranalys (eng: hierarchical agglomerative clustering) delade upp gång i sex distinkta kluster, varav fyra hade lägre hastighet än kontroller. Med hjälp av förklarbara AI (eng: explainable AI) identifierades det att fotledsvinkeln i svingfasen hade störst påverkan om vilken kluster som gångmönstret hamnat i. Genom att kombinera klinisk undersökning med datadrivna metoder kunde vi beskriva en omfattande bild av de sex gångklustren.

Syftet med den andra manuskriptet är att kvantifiera sensoriska och motoriska faktorerans påverkan på gångförmåga efter ryggmärgsskada. Med hjälp av två Gaussian process-regressionsmodeller identiferades sambanden mellan 11 beskrivande faktorer och 2 gång prestationsmått, nämligen gångavstånd på 6 minuter samt metabola energiåtgång. Med hjälp av förklarbar AI påvisades det stora påverkan av muskelstyrka på både gångsträckan och energiåtgång. Gånghjälpmedlet samt kardiovaskulär kapaciteten hade också betydande påverkan på gångprestation. Enskilda analyser gav insiktsfull information om varje individ.

Resultaten från dessa studier visar på potentiella tillämpningar av avancerad maskininlärning och AI metoder för att analysera komplexa relationer mellan funktion och motorisk prestation efter ryggmärgsskada.

Training sensory discrimination of the skin has the potential to reduce chronic pain due to sensorimotor impairments and increase sensorimotor function. Currently, there is no such device that can systematically provide rich skin stimulation suitable for a training protocol for individuals with amputation or major sensory impairment. This study describes the development and validation of a non-invasive wearable device meant to repeatedly and safely deliver somatosensory stimulations. The development was guided by a structured design control process to ensure the verifiability and validity of the design outcomes. Two sub-systems were designed: 1) a tactile display for touch and vibration sensations, and 2) a set of bands for sliding, pressure, and strain sensations. The device was designed with a versatile structure that allows for its application on different body parts. We designed a device-paired interactive computer program to enable structured sensory training sessions. Validation was performed with 11 individuals with intact limbs whose upper arm tactile sensitivity was measured over 5 training sessions. Tactile discrimination and perception threshold were measured using the standard 2-point discrimination and Semmes-Weinstein monofilament tests, respectively. The results of the monofilament test showed a significant improvement (p = 0.011), but the improvement was not significant for the 2-point discrimination test(p = 0.141). These promising results confirm the potential of the proposed training to increase the sensory acuity in the upper arms of individuals with intact limbs. Further studies will be conducted to determine how to transfer the findings of this work to improve the pain and/or functional rehabilitation in individuals with sensorimotor impairments.

Individuals with incomplete spinal cord injury (iSCI) exhibit diverse walking capabilities due to partial sensory and/or motor impairment below the injury level. This study examined how neuromuscular weakness patterns influence compensatory gait strategies, dynamic balance(margins of stability, MoS), and joint kinetics across iSCI subgroups compared to non-disabled controls. We analyzed gait data from 21 iSCI participants previously classified into four subgroups through dynamic time warping and hierarchical clustering in our previous study.

Temporospatial parameters, anterior-posterior (AP) and mediolateral (ML) MoS, and joint kinetics were extracted. The most functional group (mild plantarflexor weakness) walked comparably to controls but exhibited elevated peak hip flexion moments, suggesting increased risk for hip osteoarthritis or femoroacetabular impingement. The moderate plantarflexor weakness group demonstrated sagittal-plane compromises: slower walking speed, smaller magnitude of AP MoS, and lower peak ankle plantarflexion moments, power generation, and positive work during stance. Two lower-functioning groups with moderate-to-severe plantarflexor weakness combined with moderate hip extensor and hip abductor weakness showed similar sagittal-plane compensations plus frontal-plane compensations, including wider step width, higher ML MoS, lower peak hip extension and abduction moments. They also exhibited lower knee negative work during swing. Notably, the most impaired group relied primarily on hip muscles (extensors, abductors, and adductors) for stance-phase work generation, contrasting with other groups and controls who predominantly relied on plantarflexors. These findings demonstrate how varying levels of neuromuscular weakness,especially at the ankle and hip muscles, affect gait biomechanics after iSCI.

Spinal cord injury (SCI) impairs sensorimotor pathways, reducing walking ability. Effects of sensorimotor impairments are complex due to interactions with other factors such as age, aids, injury level, and severity. Traditional regression analysis has commonly been used to capture the effects, but it assumes linearity and misses local feature impacts. Meanwhile, explainable AI methods like SHapley Addictive exPlanations (SHAP) can reveal feature importances globally and locally based on cooperative game theory. Additionally, Gaussian Process Regression (GPR) can handle limited data sets, a common challenge in medical studies with small sample sizes. In this study, we proposed and evaluated a framework applying GPR and SHAP to quantify how sensorimotor impairments impact post-SCI walking performance. Thirty four recruited individuals with SCI underwent a clinical assessment and a six-minute walk test with oxygen consumption measurement. We identified strong linear relationships between muscle strength and six-minute walk test performance wherein greater strength was associated with longer distance walked and lower energy costs. The findings also highlighted considerable impacts of walking aids and cardiovascular capacity on post-SCI mobility. Individual SHAP analyses quantified how neurological factors influenced walking performance for each participant. This study demonstrated that nonparametric regression and explainable AI could provide insights into the complex neurological factors affecting walking ability in persons with SCI.