Applied Scientist · Quantitative Researcher

Making sense of
human movement data

I analyze sensor data from force plates, wearables, and motion capture systems to answer questions that matter — who's at risk for falling, which athletes are compensating, and what the numbers actually mean for the people behind them. Mathematics background. PhD in movement science. A decade in sport and clinical research.

Sensor signals → statistical models → decisions Athletes, older adults, and clinical populations Running a consultancy serving real clients
ferdinanddelgadophd@gmail.com LinkedIn Google Scholar movemeasure.com
Ferdinand Delgado, PhD

Ferdinand Delgado, PhD

PhD
Movement Science (Biomechanics)
MS
Kinesiology (Sport Science)
BS
Mathematics
10+
Years in Sport & Clinical Research
Selected Work

Projects

Real-world analytical systems built for real users — not toy datasets or classroom exercises.

Sensor
Analytics

Force Plate Assessment Reports

Problem: Coaches and clinicians collecting force plate data had no efficient way to turn raw signals into interpretable, branded reports they could use with athletes and patients.

Built an end-to-end system that pulls raw force-time data via the Hawkin Dynamics API, applies signal processing (Butterworth filtering, COP centering, Savitzky-Golay differentiation), computes derived metrics (sway path length, 95% confidence ellipse, asymmetry indices, EUR, DSI), and generates multi-page PDF reports with trend analysis across sessions. A Flask interface lets non-technical users select athletes, configure tests, and generate reports without touching code.

Signal Processing COP Analysis Feature Extraction Automated Reporting Python REST API
View on GitHub →
Outcome: Deployed with D1 collegiate athletes, high school programs, and older adult clinical populations. Generates comprehensive assessment reports in under 30 seconds — replacing hours of manual analysis. Currently used as the core service offering of Move, Measure, Analyze LLC.
Clinical
Classification

Gait Deterioration & Fall Risk Classification

Problem: Standard gait assessments capture averages across an entire walk — but fall risk may be better predicted by how gait breaks down over time, especially under fatigue.

Designed a quarters-based segmentation approach for 6-minute walk test data from wearable IMU sensors (APDM Opal). Instead of averaging across the full walk, I extracted gait features per quarter to capture fatigue-related deterioration patterns — speed decline, variability changes, asymmetry shifts. Built the full analytical pipeline: feature engineering, assumption testing, effect size ranking, FDR correction, and logistic regression with ROC/AUC evaluation.

Wearable IMU Data Feature Engineering Logistic Regression ROC / AUC Clinical Research
View on GitHub →
Outcome: Novel feature engineering approach identified gait markers that discriminate fall risk in older adults (n=60). The same analytical logic — extracting temporal patterns from continuous wearable signals — applies directly to health analytics in consumer wearables.
Research
Operations

VR-Based Balance & Cognition Intervention

Problem: Can immersive VR training improve balance and cognitive function in older adults? Answering this requires a tightly controlled longitudinal study with a population that's hard to recruit and retain.

Designed and executed a multi-session intervention study (2x/week over 8 weeks) with older adult participants. Managed the full research operation: IRB protocols, informed consent workflows, equipment calibration, session-level data collection, and participant scheduling across repeated lab visits. Integrated cognitive, balance, and self-report datasets across timepoints to assess intervention effects and practice-related changes.

Longitudinal Design Human Subjects Research IRB Protocols Multimodal Data Integration Participant Retention
Outcome: Successfully executed a complex multi-visit study with a challenging population. Presented preliminary findings at the Gerontological Society of America Annual Meeting (2024). This is the operational backbone of applied research — the ability to design a study, run it cleanly, and produce trustworthy data.
Data
Pipeline

Hawkin Dynamics API → Structured Data Pipeline

Problem: Organizations using Hawkin Dynamics force plates had athlete data locked behind an API with no easy way to extract, organize, or analyze it at scale.

Built Python scripts to extract full organizational data from the Hawkin Dynamics API — athletes, teams, groups, and all historical assessment data. Handles pagination, rate limiting, and data type mapping, then outputs clean, analysis-ready Excel workbooks.

REST API Integration ETL Pipeline Data Cleaning Python
Outcome: Automates what previously required manual export and cleanup — giving practitioners immediate access to their full dataset for analysis, reporting, and decision-making.
Core Competencies

What I Do

Where domain expertise, quantitative training, and real-world application intersect.

Finding Patterns in Sensor Data

Processing raw signals from force plates, IMUs, and wearables into interpretable features. Butterworth filtering, event detection, COP sway metrics, time-series segmentation — the same workflows used in wearable product R&D and health analytics.

Statistical Modeling & Inference

Logistic regression, interaction effects, mediation analysis, multilevel modeling, effect sizes, FDR correction, ROC/AUC evaluation. Trained to ask whether a result is meaningful, not just significant.

Knowing What the Numbers Mean

A decade of working with athletes and clinical populations means I understand what's behind the data — why a gait metric changed, what an asymmetry score means for return-to-play, when a finding is clinically relevant versus statistically noisy.

Designing Studies That Produce Clean Data

PhD-trained in experimental design, IRB-approved human subjects research, multi-session scheduling, and scalable data collection. The ability to design a study that generates trustworthy data is upstream of everything else.

Communicating Results to Non-Technical Audiences

Building automated reports that translate complex analyses into clear visuals and plain-language summaries for coaches, clinicians, and executives who need to make decisions — not read code.

Building Analytical Systems, Not One-Off Scripts

From API integration to automated PDF generation, I build tools that other people can use — not notebooks that only I can run. Currently operating a consultancy where these systems serve real paying clients.

Research Output

Publications

Peer-reviewed research and published conference abstracts. The Δ symbol indicates I performed the statistical analysis.

Peer-Reviewed Articles

Delgado, F.Δ, Yu, F., Peterson, D.S., Ofori, E., MacKinnon, D.P., Belden, C., Adler, C.H., Beach, T.G., & Der Ananian, C. (2025). Apolipoprotein E, executive function, and falls across cognitive status: A cross-sectional study. Dementia and Geriatric Disorders. doi:10.1159/000548084

Ofori, E., Delgado, F.Δ, James, D.L., Wilken, J., Hancock, L.M., Doniger, G.M., & Gudesblatt, M. (2024). Impact of distinct cognitive domains on gait variability in individuals with mild cognitive impairment and dementia. Experimental Brain Research. doi:10.1007/s00221-024-06832-9

Vento, K.A., Delgado, F.Δ, & Lynch, H. (2022). Lipid profiles of college female student-athletes participating at different competition levels of organized sport. Frontiers in Sports and Active Living. doi:10.3389/fspor.2022.841096

Vento, K.A., Delgado, F.Δ, Skinner, J., & Wardenaar, F. (2021). Funding and college-provided nutritional resources on diet quality among female athletes. Journal of American College Health. doi:10.1080/07448481.2021.1947301

Delgado, F. & Der Ananian, C. (2021). The use of virtual reality via head-mounted display on balance and gait in older adults: A scoping review. Games for Health Journal, 9(6). doi:10.1089/g4h.2019.0159

Published Refereed Abstracts

Delgado, F.Δ & Greenberg, J.J. (2024). Cognitive and practice effects of immersive virtual reality use in older adults: Preliminary results. Innovation in Aging, 8(S1). Gerontological Society of America Annual Meeting. doi:10.1093/geroni/igae098.3939

Delgado, F.Δ, Yu, F., MacKinnon, D.P., Peterson, D.S., Ofori, E., Adler, C., Beach, T.G., & Der Ananian, C. (2023). Severe Alzheimer's dementia alters the relationship between executive function and falls. Innovation in Aging, 7(S1). Gerontological Society of America Annual Meeting. doi:10.1093/geroni/igad104.3588

Delgado, F.Δ, Kaczmarek, O., Trebing, S., Zarif, M., Gudesblatt, M., & Ofori, E. (2021). Exploratory cross-sectional mediation analysis of the dual-task effect of cognition on gait in individuals with memory loss. Alzheimer's & Dementia, 17(S7). Alzheimer's Association International Conference. doi:10.1002/alz.054495

Delgado, F.Δ, Der Ananian, C., & Peterson, D. (2020). Balance and reactive steps in older adults with and without self-reported musculoskeletal conditions. Innovation in Aging, 4(S1). Gerontological Society of America Annual Meeting. doi:10.1093/geroni/igaa057.1683

Delgado, F.Δ, Der Ananian, C., & Merkel, A. (2019). Changes in physical function and body composition among group lifestyle balance program participants with arthritis. Medicine & Science in Sports & Exercise, 51(6S). American College of Sports Medicine Annual Meeting. doi:10.1249/01.mss.0000561144.22005.d2

+ 4 additional published abstracts and 2 manuscripts currently under review

Academic Background

Education

PhD
Arizona State University
Exercise & Nutritional Sciences
Emphasis in Biomechanics
MS
Texas A&M University–Corpus Christi
Kinesiology
Emphasis in Sport Science
Graduate Certificate
University of New Hampshire
Data Science
BS
State University of New York–College at Brockport
Mathematics

Let's work together

Open to applied scientist, quantitative research, and data analyst roles in wearable tech, sports science, and health analytics. Also available for consulting.

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