Hip Assist Mobility System (HAMS)

Inspiration

The idea for HAMS was born from observing people who could still walk but struggled with severe fatigue and limited range of motion. Whether due to aging, accidents, or musculoskeletal disorders, many individuals retain voluntary control over their lower limbs but expend a great deal of energy trying to move. Conventional assistive devices like canes or crutches help, but they don’t actively support movement. We were inspired to ask: What if technology could not just support, but enhance the mobility people already have?

What it does

HIP ASSISTED MOBILITY SYSTEM (HAMS) is a robotic hip exoskeleton designed to assist individuals with voluntary lower-limb function—particularly older adults or those recovering from injury—by augmenting their natural walking motion. It works by providing adaptive torque assistance to the hip joints, specifically during the flexion and extension phases of walking. This means that as the user initiates a step, the exoskeleton detects movement using inertial measurement units (IMUs) and angular position sensors. It applies controlled assistive force (up to 12 Nm) to help the leg move forward during hip flexion (lifting the thigh). Passive components help return the limb during extension, mimicking a natural gait cycle. The system supports walking at typical human speeds (3–5 km/h) and dynamically adapts to the user’s effort—never forcing movement, but amplifying it when needed.

How we built it

HAM is engineered to be lightweight, modular, and biomechanically effective, providing real-time assistive torque to the hip joints across three degrees of freedom.

1. Flexion/Extension – walking forward/backward. HAM uses a powered actuator to provide assistive torque during hip flexion, which reduces the energy needed for each step. Passive elements help guide the leg back during extension, minimizing resistance and power consumption.
2. Abduction/Adduction – balance and turning. This movement shifts the leg inward and outward from the body’s midline, which is essential for maintaining side-to-side balance and making turning maneuvers. HAM includes a passive rotational joint offering controlled range of motion while supporting balance correction, especially on uneven terrain.
3. Internal/External rotation – for natural leg rotation. This allows the thigh to rotate inward or outward, contributing to more natural leg movements, especially during activities like pivoting, sidestepping, or changing direction. A flexible joint enables this rotation to occur freely, preserving comfort and mobility.

The system is worn around the waist and thighs, with a brace on each leg. Made from aluminum, the frame is strong but light enough (<3kg total) to avoid fatigue or strain and ergonomic, allowing users to wear it under or over clothing. HAM uses back-drivable actuators—electric motors with low gear ratios—that allow movement to flow naturally from the user, while still applying assistive torque when needed. This ensures safety, as users can override the system at any time, and it minimizes the risk of injury from resistance or forced motion. A microcontroller receives signals from:

1. Angular position sensors at each joint,
2. An Inertial Measurement Unit (IMU) that tracks body posture and motion,
3. Torque sensors to measure load on each actuator. The system uses this data to dynamically estimate the user's movement and adjusts torque levels accordingly. Assistance is adaptive, meaning it only helps as much as the user needs—allowing for a more natural, energy-efficient walk. The control unit is powered by a rechargeable lithium-ion battery pack, capable of supporting several hours of walking.

Challenges we ran into

1. Computing Constraints: One major challenge was running our movement algorithms—originally developed and tested in the Robot Operating System (ROS) simulation environment—on an edge device suitable for real-world deployment. We needed a lightweight yet capable solution to bridge simulation and real-time control. To overcome this, we chose the Raspberry Pi 4 and run Micro-ROS (a lightweight version of ROS for microcontrollers). This setup allowed us to execute ROS-based movement libraries and algorithms while ensuring seamless interoperability with our chosen motors and sensors, all within the limited processing capacity of an embedded system that we would also collect data from.
2. User diversity: Adapting the system to suit different body types and levels of impairment.
3. Stigma and skepticism: Overcoming misconceptions about robotic technology in low-income settings.
4. Safety and ergonomics: Ensuring the device is comfortable, responsive, and non-intrusive during real-world use.

Accomplishments that we're proud of

1. Functional Prototype Developed We successfully designed and built a working prototype of the HAMS exoskeleton. The system delivers adaptive assistive torque at the hip joint and operates reliably during real-world walking trials, meeting our key performance goals for mobility support and user safety.
2. Community-Centered Testing We conducted user engagement sessions with individuals experiencing mobility challenges, gathering feedback on comfort, usability, and perceived benefit. This hands-on testing informs refinements to both hardware and control algorithms.

What we learned

Real User Needs Go Beyond Walking — They’re About Dignity and Independence Through early testing and community engagement, we discovered that mobility isn’t just about movement—it’s about freedom, confidence, and reclaiming daily life. Those who participated in our trials valued being able to walk without exhausting themselves, but they also spoke about the emotional relief of not having to rely on others. Adaptive Support Is More Effective Than Fixed Assistance Each user had different endurance levels and gait patterns. Our adaptive torque algorithm—capable of responding in real time—was crucial in maintaining natural movement and comfort. Customization and adaptability are essential in wearable robotics.

What's next for Hip Assist Mobility System (HAMS)

1. Design Refinement & User-Centered Iteration We plan to integrate feedback from our initial pilot users to: Improve comfort and fit for different body types. Optimize the weight distribution and adjustability of the exoskeleton. Refine the control algorithms for smoother, more personalized assistance.

We aim to conduct larger-scale trials with diverse user groups— in rural and urban settings. This will help us assess real-world durability, user acceptance, and therapeutic impact over time.

Built With

• angularpositionsensors
• inertialmeasurmentunit
• microcontroller
• motors
• torquesensors