Citation: Zhu, W.; Chen, Y.; Ko, S.-T.;
Lu, Z. Redundancy Reduction for
Sensor Deployment in Prosthetic
Socket: A Case Study. Sensors 2022,
22, 3103. https://doi.org/10.3390/
s22093103
Academic Editors: Yangquan Chen,
Subhas Mukhopadhyay, Nunzio
Cennamo, M. Jamal Deen, Junseop
Lee and Simone Morais
Received: 2 March 2022
Accepted: 16 April 2022
Published: 19 April 2022
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Article
Redundancy Reduction for Sensor Deployment in Prosthetic
Socket: A Case Study
Wenyao Zhu
1
, Yizhi Chen
1
, Siu-Teing Ko
2
and Zhonghai Lu
1,
*
1
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology,
10044 Stockholm, Sweden; wenyao@kth.se (W.Z.); yizhic@kth.se (Y.C.)
2
Research and Innovation, Össur, 110 Reykjavík, Iceland; stko@ossur.com
* Correspondence: zhonghai@kth.se
Abstract:
The irregular pressure exerted by a prosthetic socket over the residual limb is one of the
major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable
sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions
and rectify the socket design. In this case study, a clustering-based analysis method is presented
to evaluate the density and layout of these sensors, which aims to reduce the local redundancy of
the sensor deployment. In particular, a Self-Organizing Map (SOM) and K-means algorithm are
employed to find the clustering results of the sensor data, taking the pressure measurement of a
predefined sensor placement as the input. Then, one suitable clustering result is selected to detect the
layout redundancy from the input area. After that, the Pearson correlation coefficient (PCC) is used
as a similarity metric to guide the removal of redundant sensors and generate a new sparser layout.
The Jenson–Shannon Divergence (JSD) and the mean pressure are applied as posterior validation
metrics that compare the pressure features before and after sensor removal. A case study of a clinical
trial with two sensor strips is used to prove the utility of the clustering-based analysis method. The
sensors on the posterior and medial regions are suggested to be reduced, and the main pressure
features are kept. The proposed method can help sensor designers optimize sensor configurations for
intra-socket measurements and thus assist the prosthetists in improving the socket fitting.
Keywords:
pressure sensor system; prosthetic socket; redundancy detection; redundancy reduction;
selforganizing map; Pearson correlation coefficient
1. Introduction
As the interface between the amputation stump and prosthesis, the prosthetic socket is
the key factor that affects the comfort level of patients. However, as the cause of amputation
and residual limb characteristics vary from patient to patient, the design and adjustment
of the socket shape remain a hard problem. Normally, the rectification of socket shape
has to consider physical conditions such as the pressure, shear stress and residual limb
volume fluctuations [
1
,
2
] during activities of daily living. The measurement and analysis
of these interface pressure loading conditions then become a key component in optimizing
the socket shape and improving the comfort level of patients.
Uneven and high interfacial pressure distribution of the prosthetic socket can lead
to many discomforts in amputees’ daily lives [
3
], while the development of the sensing
technology [
4
] helps identify such stresses. Figure 1 shows an amputee patient who
wears a prosthetic socket with the sensor measurement system in a clinical test. Such
sensor systems usually have high density [
5
–
8
] to ensure the coverage of the whole stump.
The high-density coverage may, however, introduce redundancy, which increases cost and
unnecessary complexity. Therefore, detecting and removing the dispensable sensors while
keeping the effectiveness of the pressure measurement becomes crucial in pressure studies
of prosthetic sockets.
Sensors 2022, 22, 3103. https://doi.org/10.3390/s22093103 https://www.mdpi.com/journal/sensors
