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Training Multi-Modal ML Classification Models
For Real-Time Detection of Debilitating Disease
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Nikki-Rae
Alkema, PT, DPT
@nikkidashrae
● Doctor of Physical Therapy
● Practicing in Ortho/Pelvic Health
● Movement is Medicine!
● Special interests: Biomechanics,
Technology in Healthcare
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● Are you Human or an AI?
● I want 5 Kubernetes
● Virtual Machines are Real
● Cloudy, cloudy, cloudy…
● There is storage for that!
David
vonThenen
@davidvonthenen
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4
Agenda
● Medical Case Study for ML
○ Introduce a Disease
○ Discuss Use for AI in Clinical Practice
● Video Classification Model + Demo
● Audio Classification Model + Demo
● Q&A
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5
What’s the Common Thread?
Michael J. Fox Alan Alda Mohammed Ali Ozzy Osbourne
Neil Diamond Richard Lewis Janet Reno Brian Grant
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Clinical Case Study:
74-year old male with R shoulder pain after falling
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74M: R Shoulder Pain After Falling
● Reason for referral:
○ Pain
○ Difficulty with reaching,
lifting, ADLs
● Personal factors:
○ Balance issues
○ Caretaker for his wife
● Clinical observations:
○ Using walker, shuffling
steps, soft voice, tremor
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8
More Than Meets the Eye?
● He saw ONE problem:
○ “My shoulder hurts.”
● I saw TWO:
○ Mild rotator cuff tear
○ Balance issues*
*Cause of fall → undiagnosed Parkinson’s Disease
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9
● Progressive, neurologic movement disorder with no cure
What is Parkinson’s Disease? 1,2
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10
PD - If You Know, You KNOW 1,2
● PD affects movement, making it very
recognizable
○ Slow, small, rigid movement
○ Shaking or tremors
○ Postural instability and forward flexion
○ “Masked” or flat affect
○ Quiet, slurred speech
● Biomarker testing can support (but not
replace) clinical diagnosis
Mohammed Ali
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● A (not so) hypothetical
discussion began:
Given examples of normal vs.
abnormal human movement…
→ Can AI tell the difference?
→ If so, how well?
A PT and an AI Engineer Walk Into
a Bar…
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12
AI: What’s PD Got to Do With It?
✅ AI thrives on pattern recognition
✅ People with Parkinson’s
demonstrate abnormal yet
predictable movement patterns
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13
Traditional Movement Analysis
● Systematic observation and
classifcation of biomechanical
characteristics of human movement
and posture
○ PTs study normal to know
abnormal
● IT’S PHYSICS: KINETICS + KINEMATICS
○ Joint angles, velocity, fluidity, power, efficiency, etc.
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14
Movement Analysis Labs
University of Wisconsin, Lacrosse DPT Program
High tech…
…old school?
OR Both?
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Gait Analysis
Effects of Parkinson's Disease on Walking
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16
What is Gait?
● What is gait?
○ An individual’s unique
pattern of walking
● Why gait?
○ The gait cycle is heavily
studied and analyzed
○ Parkinson’s gait is
highly recognizable
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● Gait speed and quality tell me
about your…
○ Mobility
○ Independence
○ Fall-risk
● Gait speed: the sixth vital sign
○ Predictive of mortality
Relevance of Gait 3,4
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18
Normal Gait
● Relatively symmetric
● Vertical in alignment
● Fluid
● Biomechanics within
established norms
○ Speed, cadence, step
length, joint angles
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19
Parkinson’s Gait 5
● Hypokinetic
○ Small step length
○ Reduced arm swing
● Bradykinetic
○ Slow progression
● Unstable
○ Non-fluid cadence
■ Shuffling, freezing
○ Hand tremors
● Rigid
○ Flexed posture
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Gait Classification Model
How to Build a Machine Learning Model for Video
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21
Show Me the Data!?!
I Want to Build a Model, Where Do I Get the Data?
● You Have Access to that Data, If…
○ Work in Medical Research
○ Work at a Medical Institution
○ Data Broker - Google, Meta, etc
● That Isn't Me, Now What?
○ Look for Public Datasets
■ Kaggle, AcademicTorrents, etc
○ Get Creative! For This Project…
■ YouTube, Instagram, TikTok, etc
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22
Convert Video to Data
Google AI Edge: MediaPipe Pose Landmarker
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23
Video to Data Demo
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24
Codify the Characteristics
Image Credit:
Motion Cue Analysis for Parkinsonian Gait Recognition
Taha Khan, Jerker Westin, Mark Dougherty
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25
Feature Engineering
● Acceleration/Velocity of Landmarks
○ Reduce Movement, Rigidity, etc
● Angles:
○ Elbow-Shoulder-Hip
○ Shoulder-Elbow-Wrist
○ Hip-Knee-Ankle
○ Knee-Hip-Knee
○ Etc, Etc
● Step-Length
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Data Processing Pipeline
● Video -> Frame Features to CSV
○ Using Media Pipe Pose Landmarker
○ Get 3D (x, y, z) Coordinates
● CSV File <-> Seq. Modeling
○ Movement Data Across Frames
○ Features Captured:
■ Velocity, Acceleration, Angle
Measurements, etc
○ LSTM to Capture Temporal
Dependencies
● Tuning -> Final Model
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Model Architecture
● Long-Short Term Memory Network
○ Think Time-Series Data
○ KNN Imputation Fills in Gaps
○ Introduce Some Noise
● Training Steps:
○ Hyperparameter Tuning
■ Saved Parameter Grid: hidden size,
number of layers, etc
○ Systematically Evaluation
● Test Set Accuracy: 94.35%
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Normal - Inference
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29
Parkinson's - Inference
CSV
FILE
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Demo
https://youtu.be/yz8hNFlCzos
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31
So video worked…
…what about audio?
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Speech Analysis
Effects of Parkinson's Disease on Communication
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● Interaction of multiple body systems to produce verbal
communication through language
○ Voice
■ Sounds created as air passes through the vocal
chords
○ Articulation
■ Motor process of how a sound is formed in the mouth
to become words
What is speech? 6,7
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Relevance of Speech 8
● Identity
● Social engagement
● Performance in activities of daily
living
● Speech changes can result in
withdrawal, isolation, shame,
depression
*Non-verbal communication also affected in PD
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Normal Speech 7,9
● Clear, fluent, accurate articulation
● Appropriate prosody
○ The “music of language:” stress, rate, rhythm, loudness,
intonation
Alan Alda
SAG Awards, 2018
Dx: Positive
Speech: Asymptomatic
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Parkinson's Speech 8
● Hypokinetic dysarthria: changes in voice and articulation
relating to PD
○ Monotone
○ Monoloud and quiet
○ Hoarse or breathy
○ Rate abnormalities
○ Imprecision, slurring
+ Reduced facial emoting
and non-verbal gestures Alan Alda
Everything Happens Podcast with Kate Bowler , 2024
Dx: Positive, Speech: Symptomatic
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Voice Classification Model
How to Build a Machine Learning Model for Intonation
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ML Audio Classification?
● MANY Doing This!
● Spectrogram Comparison
○ Visual Representation
○ Similarities Visually
● Example Projects:
○ Cats vs Dogs
○ Environmental Sounds
○ Gunshot Recognition
Image Credit:
Fast environmental sound classification based on
resource adaptive convolutional neural network DOI:10.1038/s41598-022-10382-x
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Obtaining the Dataset
● Public Datasets:
○ NIH: Mobile Device Voice Recordings at King's College London
(MDVR-KCL)
■ Imaging: github.com/CanBul/Parkinson-Disease-Detection
○ SJTU-YONGFU-RESEARCH-GRP
■ Imaging: Enhancing Speech Recognition
● In Addition, Self Curated Dataset from YouTube:
○ Interviews
○ Podcasts
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Unique Dataset
For the Dataset Download From YouTube:
● Obtained Many Hours of Audio Data
○ Pre/Post Diagnosis of the Same Person
● Where Did We Find This Data?
○ Remember That First Slide…
○ Celebrity Interviews, Podcasts, etc
■ Tracking Sheet
● Example: Richard Lewis (2019)
○ Pre Samples: 2000-2015
○ Post Samples: 2023
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Feature Engineering
● Extracted Prosodic (Audio) Measurements:
○ Energy (RMS)
■ Root Mean Sq. = Projection, Loudness, etc
○ Formants Freq.
■ Formants = Vocal Resonance Changes
■ Mean, Standard Deviation
○ Harmonics-to-Noise Ratio (HNR), etc
■ HNR = Breathiness/Roughness
○ Jitter
■ Cycle-to-Cycle Variation in Frequency
○ Shimmer
■ Cycle-to-Cycle Amplitude Variations
○ Etc
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Data Processing Pipeline
● Recordings -> Frame Features to CSV
○ Clean Noise: SJTU-YONGFU-RESEARCH-GRP
● Extracted Prosodic Measurements:
○ Pitch, Energy (RMS), Formants Freq.,
Harmonics-to-Noise Ratio (HNR), etc
● Key: Synchronized Audio <-> Words
○ Capture Textual Context of Audio
■ Rhythm, Intonation, Stress, etc
○ Transcription With Time/Word Align
● Tuning -> Final Model
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Model Architecture
● CNN + LSTM Model
○ Convolution Neural Network (Local)
○ Long Short-Term Memory (Time-Series)
● Word/Text Embeddings
○ Word Sequence Appended to CNN
○ Each Utterance is Time Aligned
■ Captures Pacing, Intonation, etc
● Why This is Effective? Merges…
○ Short Terms Acoustics
○ Long Term Linguistic Context
○ Combine Neural Network Techniques
● Final Test Accuracy: 96.30%
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Deep Dive
● Convolutional Neural Network (CNN)
○ 2 Convolution Blocks + Pooling
○ Downsample Time ~4x
● Long Short-Term Memory (LSTM)
○ Downsampled Frames Over Time
○ Captures Continuity
● Hyperparameters:
○ Number of features: 77, CNN filters: 32, Hidden size: 128,
Number of layers: 2 Number of classes: 2, Unique
vocabulary: 4165
● Results:
○ Highlights Discriminative Features
○ Connects Acoustics to Speech
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4 Years Before - Inference
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19 Years After - Inference
CSV
FILE
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Demo
https://youtu.be/WGPW_5pClPg
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Creating a Multi-Modal ML Model
Fusion Approach (By Reusing the Code In This Repo):
1. Video Sub-Network
○ Use your LSTM-based video classifier code up to (but not including) the final classification layer.
That is, if your final LSTM outputs a hidden vector of size H, keep that as a video embedding.
2. Audio Sub-Network
○ Likewise, from the audio CNN+LSTM, grab the final LSTM output (before the classifier) as an audio
embedding of size A.
3. Fusion Mechanism
○ Concatenate the two embeddings: [video_embedding, audio_embedding] → a single vector of
size H+A.
○ Pass this fused vector through a new "fusion head" — for instance, a small MLP or another LSTM
that captures temporal modalities.
○ This network ends in a single classification layer outputting the probability of Parkinson’s vs.
Normal (or 2-class softmax).
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There Is One MoreThing…
Other Future Classification Ideas
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If Quacks Like A Duck…
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The Future is Now 10,11,12
● Attitudes mixed but warming toward AI in medicine
● Chief concerns:
(-) Job security, patient privacy,
accurate decision-making
● Great potential:
(+) Reduce administrative burden,
improve efficiency, enhance
screening
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YOU’RE INVITED TO
DREAM BIG.
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Resources
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Medical References
1. McCormack, R. Understanding the Five Stages of Parkinsons. Parkinson’s NSW. Accessed October 7, 2024.
https://www.parkinsonsnsw.org.au/understanding-the-five-stages-of-parkinsons.
2. Yun, J. Movement Symptoms. Parkinson’s Foundation. Accessed October 7, 2024.
https://www.parkinson.org/understanding-parkinsons/movement-symptoms.
3. Middleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. J Aging Phys Act. 2015;23(2):314-322. doi 10.1123/japa.2013-0236.
4. Fritz, Stacy PT, PhD1; Lusardi, Michelle PT,PhD2. White Paper: “Walking Speed: the Sixth Vital Sign.” Journal of Geriatric Physical Therapy 32(2):46-9.
5. Moore, K. Trouble Moving or Walking. Parkinson’s Foundation. Accessed October 7, 2024.
https://www.parkinson.org/understanding-parkinsons/movement-symptoms/trouble-moving
6. What is the Difference Between a Speech Evaluation and an Articulation Evaluation? Child Language and Developmental Speech. Posted October 26,
2022. Accessed March 6, 2025. https://childspeechlanguage.com/what-is-the-difference-between-a-speech-evaluation-and-an-articulation-evaluation.
7. American Speech-Language-Hearing Association. What Is Speech? What Is Language? American Speech-Language-Hearing Association.
https://www.asha.org/public/speech/development/speech-and-language/.
8. Lansford KL, Liss JM, Caviness JN, Utianski RL. A cognitive-perceptual approach to conceptualizing speech intelligibility deficits and remediation
practice in hypokinetic dysarthria. Parkinsons Dis. 2011;2011:150962. doi:10.4061/2011/150962.
9. Applebaum L, Coppola M, Goldin-Meadow S. Prosody in a communication system developed without a language model. Sign Lang Linguist.
2014;17(2):181-212. doi:10.1075/sll.17.2.02app.
10. Moldt, J.-A. et al. (2023) ‘Chatbots for future docs: exploring medical students’ attitudes and knowledge towards artificial intelligence and medical
chatbots’, Medical Education Online, 28(1). doi: 10.1080/10872981.2023.2182659.
11. Al-Medfa MK, Al-Ansari AMS, Darwish AH, Qreeballa TA, Jahrami H. Physicians' attitudes and knowledge toward artificial intelligence in medicine:
Benefits and drawbacks. Heliyon. 2023;9(4):e14744. Published 2023 Mar 23. doi:10.1016/j.heliyon.2023.e14744.
12. Appel JM. Artificial intelligence in medicine and the negative outcome penalty paradox. J Med Ethics. 2024;51(1):34-36. Published 2024 Dec 23.
doi:10.1136/jme-2023-109848.
感谢您下载包图网平台上提供的PPT作品,为了您和包图网以及原创作者的利益,请勿复制、传播、销售,否则将承担法律责任!包图网将对作品进行维权,按照传播下载次数进行十倍的索取赔偿!
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55
AI/ML Resources
CLICK HERE] for All Material Contained in this Session CLICK HERE
DigitalOcean Bare Metal H200 Availability
https://www.digitalocean.com/blog/now-available-bare-metal-nvidia-hgx-h200-gpus
Continue the Conversation - DigitalOcean Discord
https://discord.com/invite/digitalocean
Code with Instructions for Gait Model:
● Part 1 Processing Videos Features Using MediaPipe
● Part 2 Building a ML Model for Video
● Part 3 Parkinson's Gait Demo
Code with Instructions for Voice Model:
● Part 1 Processing Audio Features
● Part 2 Building a ML Model for Audio Intonation
● Part 3 Parkinson's Voice Demo
感谢您下载包图网平台上提供的PPT作品,为了您和包图网以及原创作者的利益,请勿复制、传播、销售,否则将承担法律责任!包图网将对作品进行维权,按照传播下载次数进行十倍的索取赔偿!
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56
Thank You!
David vonThenen
@davidvonthenen
https://linktr.ee/davidvonthene
n
Nikki-Rae Alkema, PT, DPT
https://linktr.ee/nikkidashra
e
@nikkidashrae

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