# Fall Detection from Audios with Audio Transformers

Prabhjot Kaur<EMAIL_ADDRESS>Wayne State
UniversityDetroitMichiganUSA43017-6221 ,  Qifan Wang<EMAIL_ADDRESS>Meta
AIMenlo ParkCaliforniaUSA and  Weisong Shi<EMAIL_ADDRESS>Wayne State
UniversityDetroitMichiganUSA

(2022)

###### Abstract.

Fall detection for the elderly is a well-researched problem with several
proposed solutions, including wearable and non-wearable techniques. While the
existing techniques have excellent detection rates, their adoption by the
target population is lacking due to the need for wearing devices and user
privacy concerns. Our paper provides a novel, non-wearable, non-intrusive, and
scalable solution for fall detection, deployed on an autonomous mobile robot
equipped with a microphone. The proposed method uses ambient sound input
recorded in people’s homes. We specifically target the bathroom environment as
it is highly prone to falls and where existing techniques cannot be deployed
without jeopardizing user privacy. The present work develops a solution based
on a Transformer architecture that takes noisy sound input from bathrooms and
classifies it into fall/no-fall class with an accuracy of 0.8673. Further, the
proposed approach is extendable to other indoor environments, besides
bathrooms and is suitable for deploying in elderly homes, hospitals, and
rehabilitation facilities without requiring the user to wear any device or be
constantly ”watched” by the sensors.

Neural Networks, Transformer, Human Fall Detection, Audio Signal Processing

††copyright: acmcopyright††journalyear: 2022††doi:
XXXXXXX.XXXXXXX††conference: IEEE/ACM international conference on Connected
Health: Applications, Systems and Engineering Technologies; November 17–19,
2022; Washington, D.C.††booktitle: IEEE/ACM international conference on
Connected Health: Applications, Systems and Engineering Technologies, November
17–19, 2022, Washington, D.C.††price: 15.00††isbn:
978-1-4503-XXXX-X/18/06††ccs: Computing methodologies Supervised learning by
classification††ccs: Computer systems organization Robotics

##  1\. Introduction

A Fall is ”an event which results in a person coming to rest inadvertently on
the ground or other lower-level” ((2021), 2021). The current estimates from
the World Health Organization (WHO) show that falls are the second major cause
of death globally, with approximately 684,000 individuals dying every year.
While people of all ages are at risk of falls and related injuries, the
population aged 60 and older suffers the highest death rate due to a fall
event. Additionally, non-fatal falls are equally devastating as they may lead
to a permanent disability, psychological fear of falling, and hospitalization
among individuals who have experienced a fall previously. About 37.3 million
falls require medical attention each year, and 0.65 million result in deaths
globally ((2021), 2021). Such consequences of falls make them a significant
public health problem that needs attention. While falls are not always
preventable, their negative impact can be minimized with a prompt response,
including detecting a fall event and notifying health support services.

There is a keen interest from both academia and industry in developing
techniques for fall detection amongst the elderly (Wang et al., 2020). The
goal is to detect a fall within a reasonable period after its occurrence so
that mitigation steps can be taken to make the consequences of a fall less
severe. Such techniques rely on different sensors, broadly categorized into
wearable and non-wearable sensors. The user must wear wearable sensors to
facilitate the collection of accelerometer/gyroscope data for further
analysis. The non-wearable sensors, on the other hand, are external sensors,
including vision-based, floor-based, and acoustic sensors. The external
sensors monitor the surroundings in their vicinity to detect if a fall event
has occurred. Besides the types of sensors used, many different algorithms and
methods exist to process the collected data to classify if the sensor input
represents a fall. These methods range from traditional statistical methods to
more advanced machine learning (Usmani et al., 2021) and deep learning (Islam
et al., 2020), (Ribeiro et al., 2022), (Lafuente-Arroyo et al., 2022) based
methods. The wearable and vision-based techniques have certain limitations.
For example, it is not always feasible to collect data with a wearable
recording device as the user may forget to put it on or may need to take it
off for performing some activity. Similarly, the vision-based sensors are not
scalable as the subject has to be in the sensor’s line of sight to be
detected, and they invade the user’s privacy. These limitations are even more
prominent in the bathroom environments, where falls amongst elderly are the
most frequent (Molés Julio et al., 2020), (Shaharudin et al., 2018), (Muhammad
Iqbal et al., 2020), (Moreland et al., 2021). Thus, a fall detection system
for the elderly that is accurate, scalable, and preserves user privacy remains
an open research problem despite the number of sensors and algorithms
developed to date. This paper proposes a non-wearable and scalable solution
for fall detection in bathroom environments, implemented on an autonomous
mobile robot. The core of the proposed methodology is an ambient sound input
that is analyzed to detect fall events. The use of sound input for fall
detection offers an advantage over wearable and vision-based sensors. It does
not require wearing a recording device, is less invasive than the vision-based
methods, and does not require the subject to be in sight to detect falls.
Further, the proposed solution is highly scalable as it is implemented on an
autonomous mobile robot, thus allowing us to detect a wider range of locations
in indoor environments.

###  1.1. Problem statement

The overall objective is to detect falls in bathroom environments that are
highly prone to fall events among the elderly. We formulate the problem as an
audio classification problem consisting of two classes, namely, fall
(positive) and no-fall (negative) classes. The goal is to develop a method
that takes audio from the target environment as input and correctly classifies
the input into one of the two classes. If a fall event is detected, an alert
must be sent to the registered party for their attention.

###  1.2. Motivation and Contribution

Our motivation is to fill the gap in the existing research within the fall
detection domain, as the current techniques have limitations regarding their
deployment efficacy in bathroom environments. To overcome the need to wear a
device and respect user privacy, we use sound input to solve the audio
classification problem. Specifically, we develop and train a deep learning
model based on the Transformer (Vaswani et al., 2017) architecture that learns
to identify fall events from the ambient sound input. The trained model is
deployed on an autonomous mobile robot capable of navigating to the target
(bathroom) environment and sending a phone text to an appropriate party if
help is needed. The main contributions of the paper are as follows:

  * •

A non-wearable, non-invasive, and scalable method for detecting fall events in
bathroom environments implemented on an autonomous mobile robot.

  * •

A novel audio classification Transformer model that takes noisy environmental
sounds as input and correctly classifies them into fall and no-fall classes
with an accuracy of 0.8673.

  * •

A new set of features for audio classification, which we refer to as “Diff”
features, that show better performance than the traditional features such as
log mel spectrograms and raw audio features commonly used for audio
classification (Droghini et al., 2018).

It is important to note that while the developed method is demonstrated for
bathroom environments, it is extendable to all indoor environments with
appropriate training data.

The remainder of the paper is structured as follows. Section 2 summarizes the
related works. Section 3 provides details on the audio dataset used in our
work, followed by Section 4 that describes the methodology developed to detect
fall events using the audio input. Section 5 focuses on implementation details
of the proposed method in the real world. Section 6 dives into the results of
the method developed in this paper. Finally, Section 7 concludes the work.

##  2\. Related Works

Research in fall detection has gained much momentum in the past few decades.
Many researchers have proposed several solutions to detect falls among the
elderly. This section briefly reviews various sensors and algorithms used in
this space. We emphasize on the more recent works and direct the reader to
existing literature surveys (Ren and Peng, 2019), (El-Bendary et al., 2013),
(Chen et al., 2018), (Wang et al., 2020), (Zhang et al., 2013) for a more
depth historical advancements in this domain.

###  2.1. Fall detection sensors and algorithms

The techniques for fall detection are classified into two categories based on
the underlying sensor technology, namely wearable and non-wearable (external)
(Delahoz and Labrador, 2014). The wearable techniques monitor human movement
by constantly collecting accelerometer and gyroscope data from devices the
user wears. Such techniques commonly use threshold-based methods to
distinguish falls from the normal movement of a human subject. Various
literature surveys such as (Karar et al., 2022), (Delahoz and Labrador, 2014),
(Gray, 2021) provide a thorough research in this area. One of the major
advantages the wearable sensors offer is that they are portable, making them
highly scalable as long as the user wears the device. Additionally, wearable
sensors are cost-effective. It is established that wearable technology
performs quite well, gaining more than 90% accuracy for detecting falls (De
Raeve et al., 2022), (Pillai et al., 2022), (Miawarni et al., 2022), (Wu et
al., 2022). However, the natural drawback of such techniques is that they are
intrusive, as the user has to wear them at all times to facilitate data
collection. There is also a risk of forgetting to wear them, defeating the
purpose of constant monitoring. The non-wearable techniques, on the other
hand, overcome these challenges as they are non-intrusive, and there is no
concern of forgetfulness by the user. The research in non-wearable technology
is equally rich as wearable technology.

The non-wearable sensors for fall detection include wide options, such as
vision/camera-based sensors, including multi-cameras and depth cameras, that
constantly stream video of the surroundings to identify falls (Zhang et al.,
2015), (Ezatzadeh and Keyvanpour, 2017). The more recent vision-based
solutions typically formulate fall detection as a human pose estimation and
recognition problem as in (Fei et al., 2022), (Lafuente-Arroyo et al., 2022).
(Lafuente-Arroyo et al., 2022) proposes a system implemented on a mobile robot
that uses its onboard RGB camera to identify falls. The underlying method is
based on the You Only Look Once (YOLOv3) deep learning model trained on the
extended Fall Person Dataset (E-FPDS). The system achieves a recall of 98.7%
on the test set. Similarly, (Fei et al., 2022) proposes a Flow-pose Net model
which combines optical flow and human pose information to achieve more robust
detection. The experiments show that the system achieves nearly 100% accuracy
on two public datasets. The cons of using vision-based sensors, however, are
that they are sensitive to illumination and occlusion, require synchronization
for multi-cameras and invade user’s privacy (Ezatzadeh and Keyvanpour, 2017).
To overcome such limitations, researchers have also explored acoustic sensors
for indoor monitoring and fall detection (Droghini et al., 2021), (Vuegen,
2019), (Alex et al., 2021). Unlike vision-based sensors, these sensors do not
suffer from illumination and occlusion problems. Finally, there is a recent
trend that uses Internet of Things (IoT) based solutions, which aim to
overcome the shortcomings of both wearable and vision-based solutions (Karar
et al., 2022).The most recent IoT-based solution is proposed in (Ribeiro et
al., 2022). The custom-made IoT device measures floor vibrations and sound
level where it is installed to detect falls. The method achieves 92.5%
accuracy and no false negatives using Morlet wavelet and 79.01% accuracy with
some false negatives using an Artificial Neural Network (ANN). All of the
techniques mentioned above require the sensors to be installed in multiple
places for wider coverage. The use of a mobile platform with its onboard
sensors, such as in (Lafuente-Arroyo et al., 2022) provides a more efficient
solution.

Besides the sensor technology used for fall detection, multiple algorithmic
approaches exist to process sensor data to identify falls. The traditional
approaches use statistical methods. While these are easier to implement, they
are susceptible to noisy data and generate more false alarms (Usmani et al.,
2021). These approaches are also less efficient in nonlinear problems. The
more recent approaches include Machine Learning (Usmani et al., 2021) and Deep
Learning-based methods (Islam et al., 2020), (Lafuente-Arroyo et al., 2022),
(Fei et al., 2022), (Miawarni et al., 2022), (Wu et al., 2022).

###  2.2. Fall datasets

Access to fall datasets is an ongoing challenge in this domain, making it
difficult to compare different methods. Most researchers end up collecting
their own dataset to test and evaluate the proposed methods (Karar et al.,
2022), (De Raeve et al., 2022), (Casilari et al., 2017b). However, there are
few datasets for fall detection that are publicly available. In the case of
wearable sensors, SisFall (Sucerquia et al., 2017) is one of the largest
datasets. It consists of data collected from two accelerometers and a
gyroscope, fixed as one compact device to a belt on the participant’s waist.
It includes 15 types of falls and 19 types of Activities of Daily Living (ADL)
collected from 38 volunteers. Other notable datasets from wearable devices are
Mobifall, tFall, DLR (Casilari et al., 2017a), UMAFall (Casilari et al.,
2017b) datasets . Similarly, (Lafuente-Arroyo et al., 2022) releases an
extended vision-based Fall Person Dataset (E-FPDS) containing 6928 annotated
RGB images representing fall and no fall situations. The publicly available
fall datasets are predominantly from wearable devices and camera-based
sensors. Specifically, there is no publicly available audio fall dataset.
Therefore, we self-collect audio data for training and evaluating the proposed
method.

##  3\. Audio dataset

###  3.1. Data acquisition

Our work aims to detect falls in the bathrooms, using sound as input. Since
none of the publicly available datasets represent fall instances from the
target environment (bathrooms), we collect our own data to train and evaluate
the proposed solution for fall detection. Specifically, an audio dataset with
8 different scenarios is collected from 12 volunteers. We carefully define
scenarios that represent a realistic environment, thus including both non-
verbal and verbal indications of a fall event. The volunteers represent
different genders, demography, and vary between 15 to 50 years old. The
participants are instructed to mock all of the 8 scenarios shown in Table 1 to
collect audio data from the bathroom environments in their own homes. All of
the data is collected using smartphones. Each scenario in Table 1 represents
either a “fall” or a “no-fall” class. Some of the scenarios for the two
classes are expected to be difficult for the model to predict. It ensures that
the proposed solution is robust to false alarms while reliably detecting
falls.

We accumulate a total of 82 audio files after the data collection step. The
last column of Table 1 has the detailed breakdown of audio files in each
category. It is essential to highlight that the audio files collected are of
varying lengths, with the length of the longest file being 8.74 seconds.
Additionally, the activity of interest (a fall event) can occur anywhere
during the audio file duration.

Table 1. Overview of the recorded dataset. Category ID |  Activity demonstrated (Class type) |  Audio recording scenario |  Recorded data breakdown
---|---|---|---
1 |  Fall |  Running water (from shower or sink or toilet) and the person says keywords demonstrating a true Fall such as ‘Help’, ‘Misty’, ‘Call 911’, ‘Help Misty’ |  Total number of examples: 12 Person says ‘Help’, ‘Please Help’ in a loud tone as in a panic mode: 9 Person says ‘Help’, ‘Help Misty’, ‘Call 911’ in a normal/soft tone: 3
2 |  No Fall |  Running water (from shower or sink or toilet) and the person says some words not relevant to a Fall, thus demonstrating a No Fall situation |  Total number of examples: 12 Person says, ‘I am playing with water’, ‘How are you?’, ‘Could you get me a towel?’ in a normal-loudish tone: 3 Person is talking in a monotonous tone, ‘Today is a good day’, ‘Hi, Good morning’, ‘What are you doing here’, ‘Where are you Misty’, ‘Are you there. I can’t hear you’, ‘I am hungry’: 7 Person humming softly: 2
3 |  Fall |  Running water (from shower or sink or toilet) and the person screams, demonstrating a true Fall |  Total number of examples: 11 Person makes a very loud sound, ‘Ahhhh’ or ‘Ouch’ or ‘Oooo’ as if hurt: 10 Person makes a softer sound, ‘Ahhh’: 1
4 |  No Fall |  Person in the bathroom performing any activity such as cleaning bathroom/taking shower and singing or talking, etc. thus demonstrating a No Fall |  Total number of examples: 11 Person says ‘I am cleaning’, ‘what are you doing here’, ‘Hey Misty, please sing me something’, in a loud tone’: 3 Person humming softly: 3 Person singing loudly: 4 Person talking softly: 1
5 |  No Fall |  Running water (from shower or sink or toilet) with no other sound interference - just plain running water |  Total number of examples: 12 Plain running water: 12
6 |  Fall |  Running water(from shower or sink or toilet) and make a loud noise of any kind demonstrating a true Fall situation (such as a ‘bang’ when hitting the floor or wall or the door) |  Total number of examples: 11 A loud sound of something dropping on the floor:9 A softer sound of something dropping on the floor: 2
8 |  Fall |  Quiet bathroom environment with a person banging on the bathroom door, demonstrating a true Fall situation and as if asking for help |  Total number of examples: 12 Person knocks at the door and says ‘Help’ in a very loud tone: 2 Person knocks at the door multiple times and loudly: 8 A soft knock at the door and person says, ‘Ahh’ as if hurt: 1 A sound of something dropping/knock at the door and the person makes sound as if hurt: 1
9 |  Fall |  Quiet bathroom environment with a person saying key words, demonstrating a true Fall situation, such as ‘Help’, ‘Misty’ |  Total number of examples: 11 Person says ‘Help’, ‘Please come’, ‘Could you help?’ multiple times and in a very loud tone: 6 Person says ‘Help’, ‘Call 911’, Misty Help me’ in a normal/soft tone: 3 Person says ‘Help’, ‘Call 911’, ‘Call Police’ in a tone that displays distress: 2

###  3.2. Data augmentation

Data augmentation is a technique to increase the size of the dataset by
applying various transformations to the original data. It helps the deep
learning model generalize better, especially when the original dataset size is
too small. We apply several transformations to fall and no-fall audio files in
the original data. The specific transformations applied are outlined in Table
2. Some of the transformations, such as time shift, time mask, and others
highlighted in the table, apply only to the no-fall categories. It is because
these transformations can potentially destroy the legitimacy of the original
data. For example, the time-shift transformation can potentially altogether
remove a fall event from the audio file, making the augmented data for that
instance useless. Specifically, we apply 14 audio transformations to each fall
category and 100 transformations to each of the no-fall categories. After
performing data augmentation, the dataset’s size increases from 82 to 4390
data points. It includes 855 fall and 3535 no-fall data points. The
Audiomentations111https://github.com/iver56/audiomentations. Python library is
used to apply all audio augmentations.

Table 2. Data augmentation Transformation type | Description
---|---
Gaussian noise | Add noise to the samples
Gain | Increase/decrease the audio volume
Gain transition | Gradual change in volume over a specific time span
Loudness normalization | Apply a constant amount of Gain
Pitch shift | Increase/Decrease the pitch
Resample | Re-sample audio
Time stretch | Increase/Decrease the audio speed
augment1a, augment2, augment13, |
augment,14, augment15 | Apply various combinations of the transformations at once
∗Time shift | Shift audio left/right in time-axis
∗High Pass, Peaking, Low Pass, |
Band Pass, Band Stop, |
High Shelf, Low Shelf filters | Apply various filters individually
∗Gaussioan SNR | Add gaussian noise to the input
∗Reverse | Apply time inversion
∗Clipping Distortion | Clip a random percentage of samples to distort audio
∗Polarity Inversion | Multiply the audio samples by (-1)
∗Tan Distortion | Apply tanh distortion
∗Time Mask | Silence a random portion of the audio
∗Normalize | Apply a peak normalization
∗MP3 Compression | Lower the audio quality by compressing
∗7-band parametric equalizer | Adjust volume of various frequency bands
∗augment3 - augment12, |
augment16-augment60 | Apply various combinations of the transformations at once
∗ These transformations are applied to “no-fall” categories only. |
a augment1 $=$ Gain transition, Add Gaussian noise, Pitch shift in sequence. |
Some transformations are applied more than once with different parameters. |

##  4\. Methodology

The augmented dataset is pre-processed and used to train and evaluate the
proposed Transformer-based deep learning model. The tasks related to audio
processing, such as reading raw audio files and feature extraction, are
performed using librosa222https://librosa.org/doc/latest/index.html. Python
library.

###  4.1. Data Pre-processing

The raw audio files of varying lengths are read with a sampling rate of 16kHz,
which is sufficient for audio processing (Berg et al., 2021), (Baevski et al.,
2020), (Droghini et al., 2018). The sampling converts raw audio to a
1-Dimensional vector (a sampled version of the original audio) containing
t$*$16k discrete samples, where ”t” is the duration of the original raw audio.
For example, if the audio file’s duration is 8.74 seconds, it results in a 1-D
vector of length (8.74$*$16k).

Given that the original audio files in our dataset are of varying lengths, the
resulting 1-D vectors after sampling are also of unequal lengths. Therefore,
we post-pad the shorter vectors with zeros to ensure that all data points in
the dataset are of equal length. Finally, the dataset is split into three
subsets: train, validation, and test sets, with an 80:10:10 ratio. This ratio
is maintained for all the categories in Table 1. It implies that a percentage
of all fall/no-fall categories are present in each set. After splitting, the
train set contains 684 falls and 2826 no-falls; the validation set contains 84
falls and 353 no-falls; the test set contains 87 falls and 356 no-fall data
points. While the train and validation sets are used to train and evaluate the
model during the training phase, the test set is reserved for the final
evaluation of the trained model.

###  4.2. Feature extraction

The most commonly used audio features for deep learning models include log mel
spectrograms (Kong et al., 2020), (Gong et al., 2021) and Mel-frequency
cepstrum coefficients (MFCC) (Berg et al., 2021), (Meghanani et al., 2021)
features. The MFCC features are typically used for processing speech audios,
whereas the log mel spectrograms are used for a broader class of audios. These
features are considered hand-crafted features as one must define various
parameters333https://librosa.org/doc/main/generated/librosa.feature.melspectrogram.html.
such as hop length, window type, and a number of mel bins to extract such
features. This strategy affects the quality of the extracted features and
hence the model output. Some methods completely bypass manual feature
extraction and directly feed the sampled audios to the model for training and
evaluation (Baevski et al., 2020). In such cases, the deep learning model
takes the raw input (in a sampled form), and it inherently learns features
during the training phase that it deems are important for the task.

In our work, we study three different types of features. Specifically, we use
log mel spectrograms, raw audios, and a novel set of features for audio, which
we call “Diff” features. The description of each feature type is below.

####  4.2.1. Segmented raw audio

The first feature type is a segmented version of the original sampled audio.
Specifically, the original sampled audio waveform of length t seconds is
broken into sequences of length t_seg seconds. Each sequence of t_seg seconds
is then stacked vertically to create a 2-D input of size (N X D), where N is
the total number of sequences and D is the number of discrete audio samples in
each sequence. For example, if the original audio of 8.74 seconds (or 139760
discrete audio samples) long is broken into sequences of length t_seg=0.1 sec,
that results in a segmented sampled audio input of shape (87x1600). Note that
a small portion of samples (in this case, 560 discrete samples or 0.035 sec
worth of audio) is dropped from the original audio to ensure that the last
sequence contains the same number of samples as the other sequences in the
(NxD) matrix. It is acceptable as the last few samples correspond to zero
padding in most of the audios in our dataset. Fig. 1 (b) shows an example of
segmented audio derived from an original audio signal.

####  4.2.2. Diff features

The second type of feature explored in this paper derives from the segmented
sampled audio described above. The idea behind creating this set of features
is to capture the change between two consecutive audio sequences. Therefore,
we subtract two successive sequences of t_seg seconds to create a new
sequence, which captures the difference between two successive sequences. It
is repeated for all sequences in the segmented raw audio. It results in a new
matrix of shape (N-1 x D), which we call “Diff” features. These features are
fed into the deep learning model as input. Fig. 1 (c) shows an example of a
“Diff” feature derived from the segmented raw audio.

####  4.2.3. Log mel spectrogram

Finally, we also explore the log mel spectrogram feature. A log mel
spectrogram is attained by taking the Fourier transform (Fast Fourier
Transform (FFT)/ Short Time Fourier Transform (STFT)) of the original audio
signal in the time domain, then converting the resulting spectrogram to a Mel-
scale, followed by applying a logarithmic operation to the Mel-scaled
spectrogram. Several parameters, including the length of the STFT window,
number of mel-bins, window type, and hop length (number of samples between
consecutive frames), directly affect the shape and form of the mel-spectrogram
and hence the performance of the deep learning model (Kong et al., 2020). The
resulting log mel spectrogram is of shape (TxM), where T is the number of
frames and M is the number of mel bins. Based on (Kong et al., 2020), (Gong et
al., 2021) and experiments on our own data (Tables 6, 7), we select 64 mel
bins, hop length of 1600 (which is equivalent to 100ms resolution for a
sampling rate of 16kHz) and “Hann” window type. This configuration results in
a log mel spectrogram of shape (88x64) for an audio signal that is 8.74sec
long. Note: The shorter hop length corresponds to higher resolution and leads
to more frames (T). Similarly, more number of mel bins leads to a higher value
for M. Fig. 1 (a) shows an example of a log mel spectrogram derived from an
original audio signal.

Next, these three features are fed independently into the deep learning model
for further training and testing.

Figure 1. Three types of features (a,b,c) used for training the model.

###  4.3. Model Architecture

Our model comprises of three main blocks: an input block, a feature encoder
block, and an output block. While the feature encoder block used remains the
same regardless of the input type fed to the model, the input and output
blocks vary slightly, yielding three different overall model configurations.

####  4.3.1. Model configuration A

In this model configuration, we use segmented raw audio features from Section
4.2.1 as an input. The input size is (87x1600), where 1600 is the feature
dimension. Since we are solving a classification problem, we append a CLS
token at the beginning of the input to learn a global representation of the
entire input (Devlin et al., 2018). The CLS token is initialized to 1 and has
the same dimension as the feature dimension of the input, which in this
specific case is 1600. At this stage, the input shape is (88x1600). It is then
followed by a linear projection which maps the features to a lower dimension,
d_model. We choose d_model as 512 as in (Vaswani et al., 2017). It results in
input of size (88x512). Finally, we add a sinusoidal position encoding to the
input since the sequence order is important. This completes the input block of
the model. The input with the positional encoding is fed to the feature
encoder block.

The feature encoder block of our model is composed of several encoder layers
of a standard Transformer architecture (Vaswani et al., 2017). Each
Transformer encoder layer consists of Multi-Head Attention (MHA) and feed-
forward sub-layers. We select the number of encoder layers (Nt) and the number
of MHA heads (H) as 12 and 12, respectively, based on the ablation studies we
performed (Tables 5, 8\- 11). In addition to the input received from the input
layer of the model, the feature encoder block also receives a binary input
mask. The input mask tells the encoder block which input sequences to ignore.
It is important because the shorter audios in the dataset have been post
padded with zeros as explained in Section 4.1. If the input mask is not
provided, the encoder will not be able to distinguish between the sequences of
the input that contain the actual audio data and those that contain the zero
padding. During the training phase, the feature encoder block learns the
embedding for the input. The dimension of the learned embedding is (88x512).
We extract the embedding of the CLS token from the output of the feature
encoder block and feed it to the output block for further processing and final
classification.

The output block of the model consists of a Multi-Layer Perceptron (MLP),
followed by a final dense layer for classification. The MLP is a sequence of
dense layers containing 265, 64, and 10 neurons. The output of the MLP feeds
to the final classification layer, which consists of 2 neurons. The final
classification layer outputs logits for two possible classes, namely the
“fall” and “no-fall” classes. These logits are passed through the softmax
activation to output the predicted final class.

####  4.3.2. Model configuration B

This model configuration uses Diff features from Section 4.2.2 as an input.
The size of the input fed to the model is (86x1600). The rest of the model
architecture is the same as configuration A, described in Section 4.3.1.
Specifically, we first add a CLS token to the input, perform a linear
projection, and finally feed the input plus the positional encoding to the
feature encoder block. The embedding of the CLS token is extracted from the
feature encoder block and passed to the output block. Within the output block,
the embedding of the CLS token passes through an MLP layer and a final dense
layer for classification.

####  4.3.3. Model configuration C

The last configuration uses log mel spectrogram features, described in Section
4.2.3. The input fed to the model has the size (88x64). This configuration is
a simpler version of the model configurations A and B. Specifically, we do not
perform any linear projection in the input block and remove the MLP layer from
the output block. The d_model is set to 64. Other necessary changes include
changing the dimension of the CLS token to 64. The embedding of the CLS token
extracted from the feature encoder block is passed to the final dense layer
directly for classification.

The model construction, training, and evaluation are configured using the
Keras API and Tensorflow 2.0 in a Google Colab environment. The hyper-
parameters chosen for the training phase are per Table 3. Finally, the trained
model is saved on a local machine for use during inference at a later time.
The classification results of the trained model are discussed in Section 6.

Figure 2. The Configuration A of a proposed Transformer-based architecture
consists of three major blocks of the model. The raw waveform is sampled,
post-padded with 0s, and then segmented to derive segmented raw audio
features. The input block adds a CLS token to the input, followed by a linear
projection. Then the input with its position encoding is passed to the feature
encoder block. After passing the input through the feature encoder block, the
embedding of the CLS token is passed through an MLP and final dense layer to
make the final class prediction.

Overall model architecture.

Table 3. Hyperparameters

Hyperparameter |  araw audio and Diff |  blog mel spectrogram
---|---|---
Batch size | 20 | 20
Epochs | 10 | 10
Optimizer | Adam | Adam
Learning rate | 0.00001 | 0.00001
Encoder layers | 12 | 12
Number of heads | 12 | 6
d_model | 512 | 64
Feed forward | d_model*2=1024 | d_model*2=128
dropout rate | 0.1 | 0.1

  * a

Hyperparameters used when using Segmented raw audio and Diff features as
inputs.

  * b

Hyperparameters used when using log mel spectrograms as inputs.

##  5\. Implementation

Our intended application for fall detection is to create a robot guard for an
indoor home environment. Therefore, we deploy the trained model on a Misty II
robot shown in Figure 3. It is a mobile robot that is 0.35m in height,
equipped with several sensors. We rely on three far-field microphones located
on Misty’s head for fall detection. These microphones record the ambient sound
from the target environment (bathrooms) and send it to the saved model for
inference. If a potential “fall” is detected, the robot sends a text message
to a registered phone number. It serves as an alert to the caregiver. While we
choose to implement the proposed method on a mobile robot as it offers an
added advantage of broader sensor coverage due to its mobility, this method
can be implemented on other devices as long as it has access to a microphone.

Figure 3. Misty II robot.

##  6\. Results and Discussion

This section discusses the results of the proposed model in Section 4.3. We
use accuracy and F1-score as the metrics to assess our model. These metrics
are commonly used in research within fall classification domain (Fei et al.,
2022), (De Raeve et al., 2022), (Droghini et al., 2018), (Droghini et al.,
2021). We label the “fall” class as a positive class and the “no-fall” class
as a negative class. The definitions of the metrics used are given in the
equations below. Note that TP, TN, FP, and FN are True Positive, True
Negative, False Positive, and False Negative, respectively.

(1) $Accuracy=\frac{TP+TN}{TP+TN+FP+FN}$

(2) $F1score=\frac{2*Precision*Recall}{Precision+Recall}$

(3) $Recall=\frac{TP}{TP+FN}$

(4) $Precision=\frac{TP}{TP+FP}$

Table 4 shows the overall results using different features and the Transformer
model configurations discussed in the paper. We use a Support Vector Machine
(SVM) and a Dense Neural Network (DNN) as baselines to compare our results as
in (Droghini et al., 2018). Further, for the SVM, both linear and rbf kernels
are evaluated. The “Diff” features give the best results in terms of both
accuracy and F1-score, with an accuracy of 0.8673 and F1-score of 0.6234.
Further, our model has a higher F1-score than the SVM and DNN, regardless of
the features used. While the model outperforms both SVM and DNN, the F1-score
is lower than desired. To investigate deeper, we conduct pairwise analysis on
all of the categories in Table 1. The results in terms of a recall value for
the fall class are shown in Figure 4. It is evident that the model can
differentiate between most fall and no-fall categories. The instances where it
struggles to give high recall is when both fall and no-fall categories contain
human speech. It includes category pairs 1 and 2, 1 and 4, 3 and 2, 3 and 4, 9
and 2. It implies that the model cannot distinguish between a normal human
speech versus a speech that contains distress calls such as calling for help.
Further, the model generally has a higher recall and a lower precision. For
example, it gives a recall of 0.82 and 0.65 at precision of 0.3, using diff
and log mel spectrogram features, respectively. In our future work, we will
consider adding human speech recognition and emotion recognition from sound to
be able to handle such challenging cases.

Next, we compare the results with other state-of-the-art works in this domain.
As no public audio dataset for fall detection is available for evaluation, a
direct comparison with other works is not possible. However, we review recent
works that use audio input to detect falls to compare our work. (Collado-
Villaverde et al., 2017) uses several machine learning techniques to detect
falls. The random forest and logistic regression yield an F1-score of 86.5%.
It is important to emphasize that the fall class in the dataset used in
(Collado-Villaverde et al., 2017) only considers fall sounds that result from
an impact on the floor when a person falls on the ground. The falls are
simulated by creating a trip-over scenario and recording the sound of an
impact of a person falling on the ground as a result. The sounds representing
no-falls contain war sounds and normal human conversations. Further, (Droghini
et al., 2018) and (Droghini et al., 2021) present a more recent contribution.
The authors perform data acquisition using a special floor acoustic sensor to
collect sounds of various objects falling on the ground, including a human
fall, book, ball, chair, and aerial microphones to record other everyday
sounds such as keys, nipper, and guitar slide. The proposed method trains a
Siamese Neural Network to generate embeddings used by a kNN classifier to
classify a sound into fall or no-fall. The method achieves an F1-score of
93.58%.

Based on Figure 4, we believe that our method is at par with the above-
mentioned state-of-the-art works if human speech data is excluded from our
dataset. However, it is crucial to include human sounds as it provides an
additional modality for detecting falls. Specifically, when a fall is
accompanied by a scream or a verbal call for help, it would be prudent for the
model to detect it in addition to a sound made by an impact on the floor. The
following section conducts several experiments to identify model hyper-
parameters that give the best results.

Table 4. Results on the Test set comparing different features and methods Method | Features | Input shape | Accuracy | F1-score
---|---|---|---|---
SVM (linear kernel) | Diff | 86x1600 | 0.7254 | 0.2105
SVM (rbf kernel) | Diff | 86x1600 | 0.8169 | 0.0908
DNN | Diff | 86x1600 | 0.8054 | 0.1237
Transformer (Ours) | Diff | 86x1600 | 0.8673 | 0.6234
SVM (linear kernel) | segmented raw audio | 87x1600 | 0.7071 | 0.1352
SVM (rbf kernel) | segmented raw audio | 87x1600 | 0.8169 | 0.0909
DNN | segmented raw audio | 87x1600 | 0.6887 | 0.2765
Transformer (Ours) | segmented raw audio | 87x64 | 0.8444 | 0.5405
SVM (linear kernel) | log mel spectrograms | 88x64 | 0.7620 | 0.3658
SVM (rbf kernel) | log mel spectrograms | 88x64 | 0.7963 | 0.3101
DNN | log mel spectrograms | 88x64 | 0.7848 | 0.2166
Transformer (Ours) | log mels spectrograms | 88x64 | 0.6796 | 0.5204
Transformer (Ours) | log mel spectrograms +segmented raw audio | 87x1664 | 0.7346 | 0.5704
Figure 4. Fall/No-Fall categories pairwise prediction results (Recall) using
Diff features and weighted loss.

###  6.1. Ablation studies

The Transformer encoder used in this work consists of a standard encoder from
the original Transformer model (Vaswani et al., 2017). The number of encoder
layers and the number of attention heads are two hyperparameters we study
first. Tables 5, 8, and 9 show the model performance for different
combinations of attention heads and encoder layers for all three features.

Table 5. Results of different heads and encoder layers, using log mel
spectrograms as an input on the Test set.

Heads | Layers | Accuracy | F1-score
---|---|---|---
1 | 3 | 0.7620 | 0.2877
1 | 6 | 0.6957 | 0.3575
1 | 12 | 0.6796 | 0.5204
3 | 3 | 0.7689 | 0.3764
3 | 6 | 0.6682 | 0.3317
3 | 12 | 0.6430 | 0.4306
6 | 3 | 0.7712 | 0.3749
6 | 6 | 0.6911 | 0.3892
6 | 12 | 0.6987 | 0.4895
12 | 3 | 0.6842 | 0.3610
12 | 6 | 0.6636 | 0.3901
12 | 12 | 0.6705 | 0.5067

  * a

Log mel spectrogram parameters: Window length=2048, Hop length=1600, Mel bins
64

  * b

Input shape (TxM) = 88x64

  * c

Transformer parameters: All other hyperparameters used are per Table 3

Further, for the log mel spectrograms, the number of mel bins and hop length
are additional parameters affecting model performance. Ideally, a higher
number of mel bins and shorter hop length should yield better results (Kong et
al., 2020), which comes at a higher computational cost. We see this trend in
Table 7. However, the number of mel bins used did not increase model
performance beyond the mel bin size of 64. Considering the computational cost
and model performance, we select 64 mel bins and 1600 as hop length for the
final model that uses log mel spectrograms as input. Next, for the segmented
raw audio and Diff features, we study the segment length (t_seg) effect on the
model performance.

Table 10 shows the model performance for different segment lengths. We choose
100ms as the segment length for segmented raw audio and Diff features for the
final model.

Table 6. Results of different Mel bins for log mel spectrograms on the Test
set.

Mel bins | TxM | Accuracy | F1-score
---|---|---|---
32 | 88x32 | 0.6476 | 0.3839
64 | 88x64 | 0.6987 | 0.4895
128 | 88x128 | 0.5217 | 0.4456

  * a

Other log mel spectrogram parameters: Window length=2048, Hop length=1600

  * b

Transformer parameters: d_model=M, rest of the hyperparameters used are per
Table 3

Table 7. Results of different Hop length for log mel spectrograms on the Test
set.

Hop length | Time resolution | TxM | Accuracy | F1-score
---|---|---|---|---
1600 | 100 ms | 88x64 | 0.6987 | 0.4895
1000 | 62.5 ms | 140x64 | 0.6751 | 0.5232
500 | 31.25 ms | 280x64 | 0.7071 | 0.5428

  * a

Other log mel spectrogram parameters: Window length=2048, Mels = 64

  * b

Transformer parameters: All hyperparameters used are per Table 3

Table 8. Results of different heads and encoder layers, using segmented raw
audio as an input (t_seg = 100ms) on the Test set.

Heads | Layers | Accuracy | F1-score
---|---|---|---
1 | 3 | 0.8352 | 0.5198
1 | 6 | 0.8352 | 0.4626
1 | 12 | 0.8101 | 0.5146
3 | 3 | 0.8009 | 0.4238
3 | 6 | 0.8352 | 0.5263
3 | 12 | 0.7506 | 0.3699
6 | 3 | 0.8444 | 0.3334
6 | 6 | 0.7140 | 0.4239
6 | 12 | 0.8261 | 0.4571
12 | 3 | 0.8352 | 0.4857
12 | 6 | 0.8444 | 0.5405
12 | 12 | 0.8444 | 0.5278

  * a

Input shape (NxD) = (87x1600)

  * b

Transformer parameters: All other hyperparameters used are per Table 3

Table 9. Results of different heads and encoder layers, using Diff features an
input on the Test set.

Heads | Layers | Accuracy | F1-score
---|---|---|---
1 | 3 | 0.7643 | 0.3905
1 | 6 | 0.8467 | 0.5248
1 | 12 | 0.7849 | 0.5154
3 | 3 | 0.7941 | 0.4706
3 | 6 | 0.8490 | 0.5479
3 | 12 | 0.7803 | 0.3924
6 | 3 | 0.8238 | 0.4832
6 | 6 | 0.7941 | 0.4444
6 | 12 | 0.8146 | 0.5318
12 | 3 | 0.8581 | 0.5156
12 | 6 | 0.8284 | 0.5614
12 | 12 | 0.8673 | 0.6234

  * a

Input shape (N-1 x D) = (86x1600)

  * b

Transformer parameters: All other hyperparameters used are per Table 3

Table 10. Results of different segment length, using Diff features.

t_seg | NxD | Accuracy | F1-score
---|---|---|---
50 ms | 173x800 | 0.8490 | 0.6526
100 ms | 86x1600 | 0.8673 | 0.6234
300 ms | 28x4800 | 0.8215 | 0.2289
500 ms | 16x8000 | 0.9192 | 0.3577

  * a

Transformer parameters: All of the hyperparameters used are per Table 3

Additionally, Figure 5 shows the effect of the number of epochs on both
training and validation sets in terms of the model accuracy. We selected 10
epochs as the hyperparameter for the training phase based on this experiment.
The model achieves an accuracy of 0.9980 on the training set after 50 epochs,
whereas the validation accuracy peaks at 0.7449 after just 10 epochs.
Additionally, the test accuracy after 50 epochs is 0.7620, with an F1-score of
0.4091. Finally, we study the performance of log mel spectrograms and
segmented raw audios combined. Table 11 shows that the combined features do
not yield any better performance than using the Diff features alone.

Figure 5. Number of Epochs versus model accuracy on training and validation
sets. Table 11. Results of different heads and encoder layers, using log mel
spectrograms and raw features combined.

Heads | Layers | Accuracy | F1-score
---|---|---|---
1 | 3 | 0.7297 | 0.4000
1 | 6 | 0.7297 | 0.5234
1 | 12 | 0.7838 | 0.5512
3 | 3 | 0.7838 | 0.5092
3 | 6 | 0.7277 | 0.4636
3 | 12 | 0.7346 | 0.5704
6 | 3 | 0.7460 | 0.5316
6 | 6 | 0.8535 | 0.5151
6 | 12 | 0.8169 | 0.5652
12 | 3 | 0.7483 | 0.5000
12 | 6 | 0.7551 | 0.5485
12 | 12 | 0.7838 | 0.5569

  * a

Input shape (NxD) = (87x1664)

  * b

Transformer parameters: All other hyperparameters used are per Table 3

##  7\. Conclusion and Future Work

In this work, we investigate ambient sound input for fall detection among the
elderly. We develop and train a Transformer-based deep learning model to
detect fall events from the sound input. Our method presents a non-wearable,
non-intrusive and scalable solution for fall detection, especially for the
bathroom environments where other existing techniques cannot be relied upon,
either due to the need for wearing a device or user privacy concerns.
Additionally, we implement the solution on an autonomous mobile robot that
gives a broader sensing coverage due to its mobility. It is essential to
highlight that the proposed solution is extensible to indoor environments
beyond the bathroom with additional training. To further improve upon the
results presented in the paper, we plan on collecting more variety of fall
events and explore other audio features.

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