FallGown: A Soft Pneumatic Wearable Device for Fall Prevention
Authors: Megan C. Coram, Haoran Lei, Steven H. Collins, Steve B. Cousins, Michelle Baldonado, Cynthia Sung, Allison M. Okamura
Organizations: Department of Mechanical Engineering and Robotics Center, Stanford University, Stanford, CA, 94305, USA · General Robotics, Automation, Sensing & Perception (GRASP) Lab, University of Pennsylvania, Philadelphia, PA 19104, USA
Concerns about falling and potential resulting injuries prevent many older adults from participating in forms of health-improving exercise such as walking, dance, and tai chi. Existing passive devices like walkers require use of the arms for support, and active devices like exoskeletons and robotic frames are often rigid, bulky, and stigmatized, which limits their use. The FallGown is a soft wearable support structure consisting of pressurized pneumatic pouches within a fabric shell; it can support the full weight of a user's body and aims to prevent falls. It is portable (6.9 kg and 0.051 m3 when stored), minimally burdensome (7.24-20.8 N drag force depending on floor type and whether an optional rolling base frame is used), and convenient (less than 2.5 minutes on average to don and less than 0.5 minutes on average to doff). The optional rolling base lowers friction with the floor, but reduces portability and skirt deformation (which can be useful to fit through doors and other tight spaces). Fall prevention testing with a single user revealed a tradeoff between device size and stability against tipping; a skirt with 12 pneumatic pouches and a floor diameter of 1.4 meters with a base frame prevented all falls except in one case with a mechanical failure. On-board pressure sensors can sense the user's weight distribution, providing an opportunity for monitored exercise and adaptive support.
Figures & tables
Fig. 1: The FallGown is a soft pneumatic wearable device that can provide balance support and resist falls while (A) walking, (B) leaning, and (C) dancing.
Fig. 2: Initial FallGown design concepts that motivated the final design presented in Figs. 1 and 3 . (A) A single, narrow inflated tube buckles under the load of a user leaning on it, motivating the need for a continuous surface with robust structural design. (B) An initial FallGown prototype with a contiguous inflated body made of one layer of thermoplastic polyurethane (TPU)-coated ripstop nylon. The drawback of this approach was catastrophic failure in the cases of fabrication defects and puncture.
Fig. 3: (A) The components of the FallGown include pneumatic pouches, push-to-connect valves, an outer fabric shell, accessible zippers, a waist harness with leg straps, and an optional wheeled PVC frame. (B) A single pneumatic pouch is a folded flat sheet of 70 denier ripstop nylon coated with thermoplastic polyurethane that is ultrasonically welded on the three non-folded edges. A tube, valve, and push-to-connect bulkhead connector are placed at the top right corner for inflation/deflation access. The pouch shown here is uninflated. (C) The sewing pattern for the outer shell consists of the outer cone, the inner cone, the top ring, the bottom ring, and 13 inner spacers. Solid lines indicate cuts, while dotted lines indicate mirroring. Seam allowance must also be added to the patterns according to the sewist’s preference. (D) A 3D rendering of the fabric shell shows how the inner spacers are evenly distributed around the device. In the front of the skirt, two spacer panels are exposed on the exterior surface to create the zippered opening used to don and doff the device.
Fig. 4: FallGown form factor compression tests performed with an MTS testing machine. (A) Bottom views, (B) side views in the uncompressed state, and (C) side views in the compressed state for the 4-segment, 8-segment, and 12-segment prototypes, from left to right. (D) Compressive force experienced by the prototypes as a function of compression distance.
Fig. 5: Size comparison between the device when fully inflated and fully deflated, with a 12-ounce (355 mL) soda can (12 cm tall) for comparison.
Fig. 6: To don the FallGown, one must (A) put on the harness which goes around the waist and upper thighs, (B) step through the opening, (C) zip up and buckle the front closure, and (D) buckle the harness to the top of the skirt.
Fig. 7: The drag force (burden) measurement setup, in which a force gauge is used to pull the skirt at a constant speed against friction horizontally across a floor.
Floor Type
Wood
Tiles
Carpet
Wheels on Tile
Drag Force (N)
20.8
18.6
29.0
7.24
% of Body Weight
2.59
2.32
3.61
0.90
Metabolic Rate % Increase
30.3
27.0
43.2
11.1
TABLE I: FallGown Movement Burden
Fig. 8: (A) The simulated fall setup, where the user’s center of mass tips over the edge of the skirt and down to the mattress. (B) A backward fall in the 8-segment skirt, and (C) A prevented backward fall in the 12-segment skirt.
Number
Base Diameter
Fall Direction
Success Rate
of Segments
(cm)
8
103.5
Front
0/3
8
103.5
Side
0/3
8
103.5
Back
0/3
10
125.5
Front
0/3
10
125.5
Side
2/4
TABLE II: Fall Prevention Tests
Fig. 9: Pressure measured in the twelve pneumatic pouches during (A) walking, (B) leaning forward, (C) leaning to the user’s left, (D) leaning to the user’s right, and (E) leaning backward. The seam between segments 1 and 12 is at the front center of the FallGown.
Older adults are particularly susceptible to falls following perturbations during standing, such as forward loss of balance. Back support devices that assist trunk extension may help mitigate fall risk by preventing excessive trunk flexion. Previous studies have investigated heavy back support devices; however, these systems often introduced adverse effects on stability due to their added mass, which shifted the body's natural center of mass unfavorably. In contrast, lightweight passive devices have shown limited benefits, as they can generate only modest assistive forces during the relatively small trunk flexion associated with forward balance loss. In this study, we evaluated the effects of a lightweight semi-active soft back support device on postural stability following standing perturbations. Our device combines an active element (a pneumatic artificial muscle) in parallel with a passive elastic band. The active element rapidly provides assistive force following a perturbation, overcoming the limitations of passive devices. Experiments conducted with five healthy individuals demonstrated that the semi-active device significantly reduced whole-body angular momentum and increased the margin of stability, indicating improved balance recovery performance. These results highlight the promise of semi-active soft wearable robots as an effective and lightweight strategy for fall prevention during standing perturbations.
Rohan Khatavkar, Jiefeng Sun, Hyunglae Lee
The authors are with the School for Engineering of Matter, Transport and Energy, Tempe, AZ 85287, USA.
The effectiveness of a soft wearable back-support device in enhancing postural stability was investigated under trip-like perturbations using two experimental paradigms: perturbed standing and perturbed walking. Healthy subjects completed trials under three different back-support conditions: no device, device worn with low stiffness, and device activated with high stiffness. Whole-body stability was quantified using the minimum Margin of Stability (MOS) at the point of maximal instability. Results demonstrated increased MOS during device use, indicating enhanced postural stability. In standing, MOS increased significantly with device stiffness, whereas in walking, both device conditions improved MOS relative to no device but did not differ significantly from each other. These findings highlight the potential of soft wearable back-support devices with adjustable stiffness to improve reactive balance control against external perturbations, with important implications for fall prevention. Future research should explore personalized stiffness optimization and evaluate efficacy in populations at elevated risk of falls.
Yuanhao Chen, Rohan Khatavkar, Soubhagya Nayak +2
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USA
Existing deep learning approaches for wearable fall detection systems rely on self-attention mechanisms that impose quadratic computational overhead, distributing weights across all time steps. This global weight distribution impairs the precise localization of the brief impact signatures that characterize falls within short, fixed-length windows. To overcome this challenge, we propose Gated-CNN, a lightweight dual-stream architecture that processes accelerometer and gyroscope streams through independent one-dimensional convolutional feature extractors, followed by (i) a sigmoid gating module that selectively suppresses uninformative background activations while amplifying fall-discriminative features, (ii) a global average pooling layer that compresses each stream into a compact fixed-length descriptor, and (iii) a shared classification head that fuses both descriptors for binary fall prediction. For offline evaluation, we evaluate the model across five wrist-mounted inertial measurement unit (IMU) datasets, achieving average F1-scores of 93%, 93%, 90%, 91%, and 90% on SmartFallMM, WEDA-Fall, FallAllD, UMAFall, and UP-Fall, outperforming Transformer baselines. For real-time evaluation, we deployed the model on a Google Pixel Watch 3 and tested across 12 participants. The model achieves an average F1-score of 97% and an accuracy of 98% with zero missed falls, showing that sigmoid gating offers a more structurally aligned and computationally efficient alternative to attention for commodity smartwatch-based fall detection.