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# Toward Seamless Navigation for Visually Impaired People: Personalized Gait Learning and Smartphone-Based Guidance

Valerie Renaudin

► To cite this version:

Valerie Renaudin. Toward Seamless Navigation for Visually Impaired People: Personalized Gait Learning and Smartphone-Based Guidance. 2025, 21p. hal-05303026

HAL Id: hal-05303026

https://hal.science/hal-05303026v1

Submitted on 8 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
16 September 2025

RENAUDIN Valérie

and all GEOLOC team
members

Toward Seamless Navigation
for Visually Impaired People:
Personalized Gait Learning and
Smartphone-Based Guidance

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Université
Gustave Eiffel

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A CENTURY OF NAVIGATION AIDS FOR
THE VISUALLY IMPAIRED

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Université
Gustave Eiffel

2
FROM CANES TO AI: THE EVOLUTION OF ASSISTIVE TOOLS

Renaudin Valérie®

19
16

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19
21

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60

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90

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30

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GUIDE DOG

First Guide Dog training
center for the war-blind in
Oldenburg, Germany, run
by Dr. Gerhard Stalling.

WHITE CANE

Invented by British
photographer James Biggs
in 1921. Over time, the
white cane has been
instrumented with sensors,
especially to detect distant
obstacles.

ULTRA SOUND
GLASSES

Electronic Travel Aids are
introduced in 1940. The
Sonic Glasses is invented
by Dr. Leslie Kay in 1960.
It detects obstacles with
ultrasonic waves.

DIGITAL
ASSISTIVE
TECHNO.

Introduction of digital
technology for assistive
devices such as screen
readers, Braille displays,
GPS-based navigation
apps, and object
recognition systems
supported by artificial
intelligence.

AI-BASED
NAVIGATION

Artificial Intelligence is
used to enhance personal
senses, improve navigation
skills and is integrated into
everyday clothing and tools

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Université
Gustave Eiffel

3

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Renaudin Valérie®

4

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Université
Gustave Eiffel
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## IMAGE BASED MOBILITY AIDS

### COMMON DESCRIPTION OF THE SURROUNDINGS ON REQUEST

#### Be My Eyes

Connect visually impaired people with sighted volunteers via a live video call.

Volunteers describe what the camera sees, helping them find their way in a new environment

#### AIRA

Paid services where visually impaired people receive help from trained agents who are in a remote location

The agents use the camera image from the user's smartphone or smart glasses to help in various situations

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### AI FOR THE INTERPRETATION AND DESCRIPTION OF THE SURROUNDING WORLD

#### Seeing AI

Recognize and describe the world around the user.

Functions: Reading text, recognizing products, describing scenes, even people and their emotions.

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#### Envision AI

Recognize and speak text, objects, and people.

Functions: Reading documents, recognizing friends and describing scenes

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### VISUAL LANDMARKS FOR POSITIONING

#### GoodMaps

High-precision indoor maps created with LiDAR

Recognition of visual markers to determine location and orientation

Audio for guidance instructions and descriptions

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#### NaviLens

Recognition of high-contrast color codes QR codes

Audio description and relative positioning to the visual marking

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GNSS BASED MOBILITY AIDS

GPS AND SURROUNDING
DESCRIPTION

Ariadne GPS

Real-time position and street information.
Interactive talking map feature.
Street names and numbers are
Map navigation by finger
"Monitor" function for periodic position
updates.

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Seeing Eye GPS

Accessible turn-by-turn GPS app for iPhone.
Tailored navigation features for blind users.
Verbal announcements at intersections, POIs
iOs only
Battery life impact due to GPS usage in the
background

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GPS AND SENSOR
FUSION

GoogleMaps

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Apple Maps

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BEACON BASED MOBILITY AIDS

NAVIGATION WITH BLE SIGNALS
AND SENSOR FUSION

Evelity

Turn-by-turn navigation instructions

Fusion of BLE signals and inertial signals

Itineraries designed to account for furniture
and indoor obstacles

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Step Hear

Audio alerts passing by a Step-Hear device

Signal via Bluetooth: location details, service
information, and navigation instructions

Cloud information

Personal Wrist Activator
with RFID technology

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OBSTACLES SENSING WITH
INSTRUMENTED CANE

WeWalk

Smart Cane Integration

Control cane features with a built-in touchpad via a
custom app

One-hand remains free and phone stays in pocket
Detects obstacles, Navigation guidance

**Use of public transportation**

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UltraCane

Dual Range, Ultrasonic with narrow beam

Navigation guidance is not supported

Obstacle detection

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ARTIFICIAL INTELLIGENCE TO ADDRESS NAVIGATION CHALLENGES FOR
THE VISUALLY IMPAIRED

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Today's Navigation Systems
for the Visually Impaired

Current Limitations

Lack of dynamic interaction and
adaptability to changes

No seamless indoor and outdoor
operation

Inaccurate - unreliable positioning

Complex without addressing the specific
needs of the visually impaired

The needs of Visually Impaired

Reliable and accurate positioning

Trustworthy system

Easy usage with a simple interface

Reduced complexity for user comfort

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Artificial Intelligence
to Tailor the Solutions

AI as a Promising Avenue

Harnessing low cost sensors' data
from smart gadgets, future smart
textiles such as clothing or shoes

Transform personal motion data into
personalized guidance

Methodology

Complex methods considering
human behavior for development

Unique adaptability to individual
needs offering a tailored experience

Use engaged AI

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Fu H et al. (2023), "Analysis of the Recent AI for Pedestrian Navigation With Wearable Inertial Sensors", IEEE Journal of Indoor and Seamless Positioning and Navigation.

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Université
Gustave Eiffel

OUR VISION AND PRINCIPLES

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Okeena

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Université
Gustave Eiffel
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**Objective:** To improve guidance for visually impaired people using smartphones and contribute to a more inclusive society

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**Our vision:** Technology that adapts to people, not the other way around

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**Our founding intuition:** Integrate individual mobility characteristics into algorithms, building on real walking profiles influenced by disability

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**And to do that...** We need data! Walking data, in real-life situations, with the users concerned, to design individualized and robust models

15

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Université
Gustave Eiffel

# CO-DESIGNING WITH VISUALLY IMPAIRED PEOPLE

## Construction with participants

* Meetings and three feedback workshops to understand the needs of visually impaired people

* Discussions on uses: location technology, guidance, safety

* Compilation of their experiences and expectations

* Sharing of mutual expert opinions

## Field experiments

* Test sessions on the Nantes campus and in real-world environments: sidewalks, parks, public spaces

* Collection of movement data (inertial signatures) and feedback from participants

* Progressive adjustments based on user observations

## Development and validation of tailored solutions

* Design of a smart pedometer to improve navigation

* Integration of user feedback into the final design of the prototype app in Evelyn

* Demonstration of results through practical tests and a prototype guidance application

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Université
Gustave Eiffel
HOW MUCH DATA WAS NEEDED?

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TEAM
GEOLOC

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Université
Gustave Eiffel

BUILDING A TEST TRACK TO LEARN FROM REAL-LIFE MOBILITY

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TEAM
GEOLOC

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Data collection

Since late 2023, we have created walking routes on the Nantes campus to conduct safe experiments with elements specific to crossing roads and moving around with a visual impairment. This test track should enable us to accelerate our research.

**Length:** adjustable, from 600m to 1km

**Environment:** outdoor & indoor

**Crossing elements:** bus stop, elevator, tactile warning strip, reserved corridor, audible traffic signals, step nosings, crossings

**Funding:** ANR, Okeenea, Eiffel University

**Design assistance:** Ocens

**Learning takes place on the test track in a secure environment,** e.g., walking model and pedestrian crossing recognition, also works in the city

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Université
Gustave Eiffel
BUILDING KNOWLEDGE STEP BY STEP
5 YEARS - 26 PARTICIPANTS - 350 KM

**Teamwork!**

Many people are needed to carry out the tests

**Population diversity**

**26 people** with visual impairments

**Ages** 38 to 74

**Disability Levels:** Categories 1 to 5 (WHO)

Male/female ratio: 65%/35%

**Collected Dataset**

**5 Locations:** Nantes campus, Île de Nantes, Lyon

**Duration:** almost 5 years (2021-2025)

**Hardware used:** ULISS & smartphone

**Data type:** inertial, visual, GNSS, and cartographic

**Human Body Location:** feet, head, pockets, hands, and arms

**Trajectory length:** 600 - 1,000 m

**Record Track:** 425 over 44 days

**Total:** almost 350 km of walking

<figure><img src="image_75.png" /><figcaption>Lyon 2021</figcaption></figure>

<figure><img src="image_76.png" /><figcaption>Nantes Campus, 2022</figcaption></figure>

Renaudin Valérie

<figure><img src="image_77.png" /><figcaption>Île de Nantes 2023</figcaption></figure>

<figure><img src="image_78.png" /><figcaption>Île de Nantes 2024</figcaption></figure>

<figure><img src="image_79.png" /><figcaption>Nantes Campus 2025</figcaption></figure>

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19

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Université
Gustave Eiffel

BREAKING AWAY FROM EXISTING SOLUTIONS

*From infrastructure-heavy to lightweight and adaptive*

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Université
Gustave Eiffel
THREE BREAKTHROUGHS DRIVING OUR RESEARCH

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EVERY STEP COUNTS:
MAKING PEDOMETERS
RELIABLE

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M. I. SAYYAF
Energy-efficient AI-
based localization on
embedded devices
with multi-sensor
fusion, 2026

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6 articles published
3 articles planned

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TURNING GAIT INTO
PERSONALIZED
NAVIGATION

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H. FU, Continuous
learning of kinematic
walking models from
inertial sig. to improve
navigation for people
with disabilities, 2024

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6 articles published
2 articles planned

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FUSION WITH VIRTUAL
INERTIAL LANDMARKS

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M. WANG, Learning
& recognition of
landmarks 2 improve
mobility & position.
for visually impaired
people, 2025

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6 articles published
3 articles planned

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## Why a precise pedometer?

* We use step times to divide up the inertial measurements recorded by the phone

* For accurate guidance in urban environments, visually impaired people need to be able to rely on every step

* An error of 10 steps can mean missing a turn or a crosswalk

## Our solution: SmartStep

* Development of a robust pedometer based on an anomaly filter and an autoencoder model

* Integration into a secure pedestrian navigation system

## Results

* 30% fewer step detection errors compared to Google's "step_counter" - sensorManager API

* Out of 100 steps: 7 steps missed instead of 23

* Works for all users: able-bodied, visually impaired, different smartphone positions

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Université
Gustave Eiffel

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## OPEN SOURCE CODES AND SMARTSTEP APP AVAILABLE ON GOOGLE STORE

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<figure><img src="image_103.png" /><figcaption>Geoloc
Smart
Step</figcaption></figure>

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Université
Gustave Eiffel

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TURNING GAIT INTO PERSONALIZED
NAVIGATION

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TEAM
GEOLOC

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Université
Gustave Eiffel

Renaudin Valérie®

WE ALL HAVE AN INERTIAL SIGNATURE OF OUR GAIT
CORRESPONDING TO THE MOVEMENT OF THE DEVICE

<figure><img src="image_110.png" /><figcaption>Volunteer M1, right pocket of vest</figcaption></figure>

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<figure><img src="image_115.png" /><figcaption>Volunteer M2, smartphone in left hand</figcaption></figure>

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TEAM
GEOLOC

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Université
Gustave Eiffel
# SWITCHING FROM INERTIAL GAIT FINGERPTINT TO MOVEMENT

Each pattern recorded in the sensors corresponds to a repetitive walking motion

Thanks to artificial intelligence, this movement can be recognized and reconstructed

This movement is translated into a displacement, which includes two essential elements:

* Direction: where the person is going

* Stride length: how many meters they advance with each step

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# Estimating displacement from learned gait signature:
Mobility Adapted Pedestrian Inertial Navigation (MAPIN)

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TEAM
GEOLOC

<figure><img src="image_125.png" /></figure>

### Step 1

Segmentation by
stride
(smartstep)

<figure><img src="image_126.png" /></figure>

### Step 2

Project the acc on
the map
background with
MAGYQ

<figure><img src="image_127.png" /></figure>

### Step 3

Filtering to
remove signal
noise

<figure><img src="image_128.png" /></figure>

### Step 6

Reconstructing the
person's trajectory
using stride vectors

### Step 5

Stair detection

<figure><img src="image_129.png" /></figure>

### Step 4

Estimate the
stride vector
using a custom
AI model
(LSTM)

<figure><img src="image_130.png" /></figure>

Université
Gustave Eiffel
# INERTIAL ONLY POSITIONING ACCURACY

## State of the art

* AI-based methods dominate the inertial based positioning market

* The performance of existing generic methods is not consistent for all users and can vary from 7 cm to 30 cm per stride (i.e., 2 steps)

## Our results

* A study of gait fingerprint stability in time (18 months) for five people (2 with DV, 3 without DV) shows high similarity for the same person and a much greater deviation, i.e. double for this person with other DV

* The MAPIN error is consistent: 5-9 cm per stride for all users

* Histogram calculated over **43 km with 5 people and 3 different locations** of the Google Pixel 6 phone

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Université
Gustave Eiffel

29

# FUSED POSITIONING WITH VIRTUAL INERTIAL LANDMARK DETECTION

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Université
Gustave Eiffel
## FUSED POSITIONING WITHOUT ARTIFICIAL MARKERS

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Inertial only positioning requires external information to achieve accurate positioning and mitigate dead reckoning error accumulation

Correction is usually done external information, such as satellite data, wireless signals or visual information

However, these sources require additional hardware and may be prone to errors in certain situations (multipath, obstacles, etc.)

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Université
Gustave Eiffel

## SPATIAL ORIENTATION LANDMARKS FOR VISUALLY IMPAIRED PEOPLE

A urban landmark contributing to the navigation of visually impaired people

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12 participants (blind: 6, visually impaired: 6)

36 routes: Each person described three routes they regularly travel, lasting approximately five minutes and covering a distance of approximately 500 meters

This interview enabled 90 landmarks to be collected

<figure><img src="image_143.png" /></figure>

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<figure><img src="image_144.png" /></figure>

Université
Gustave Eiffel
# CONSTRUCTION GRAPHS FOR VISUALLY IMPAIRED PEOPLE

## Overview of interviews with visually impaired people

### Mobility Instructors

Usable virtual markers: doors, corners, bus stops, crosswalks, traffic lights, tactile strips

### Routes for the visually impaired

- Open map data is captured and merged into navigation graphs

- Secured navigation paths for the visually impaired people

### Objective

- To facilitate street crossing and urban navigation

- Connect inertial virtual landmarks (nodes) to support the navigation of visually impaired people

<figure><img src="image_145.png" /></figure>

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<figure><img src="image_146.png" /></figure>

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Université
Gustave Eiffel

# DETECTION OF PEDESTRIAN CROSSINGS USING ONLY INERTIAL DATA

<figure><img src="image_148.png" /></figure>

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<figure><img src="image_149.png" /></figure>

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Université
Gustave Eiffel
## DATA FUSION USING PARTICLE FILTERS

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Université
Gustave Eiffel

35

## DATA FUSION WITH PARTICLE FILTERS

Average accuracy with different types of data

* Without correction: 7.2 m

* Correction with detected reference points: 3.21 m (preferred case)

* Correction with a precise graph: 2.30 m (non-preferred case)

* Correction with all data: 1.08 m (ideal case)

A 1.08 m average positioning accuracy, sufficient for transferring the algorithms into a navigation app

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Université
Gustave Eiffel

36
FROM RESEARCH TO REALITY

<figure><img src="image_157.png" /></figure>

TEAM
GEOLOC

<figure><img src="image_158.png" /></figure>

<figure><img src="image_159.png" /></figure>

Université
Gustave Eiffel

<figure><img src="image_160.png" /></figure>

Renaudin Valérie

TEAM
GEOLOC

<figure><img src="image_161.png" /></figure>

<figure><img src="image_162.png" /></figure>

Université
Gustave Eiffel

<figure><img src="image_163.png" /></figure>
APPLICATION

<figure><img src="image_164.png" /></figure>

MAPIN
+
EVELITY

<figure><img src="image_165.png" /></figure>

<figure><img src="image_166.png" /></figure>

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## WHAT'S NEXT

### Smarter Maps

Automatic construction of
pedestrian route networks

### Recalibration

Integrating pedestrian
interaction with street
furniture for absolute
recalibration

### Symbiotic Interaction

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Toward Seamless
Navigation for All

"Because true mobility means
independence, dignity and inclusion"

<figure><img src="image_172.png" /></figure>

TEAM
GEOLOC

Valérie RENAUDIN

valerie.renaudin@univ-eiffel.fr

+33 (0)612 712 212

https://geoloc.univ-gustave-eiffel.fr/