Indoor Localisation for Detecting Medication Use in Parkinson's Disease
Authors: Ferdian Jovan
Catherine Morgan
Ryan McConville
Emma Tonkin
Alan Whone
Ian Craddock
Venue: Submitted to ICLR 2023 (2023)
Keywords: Transformer
Indoor Localisation
Medication State Classification
Parkinson's Disease

Abstract: Parkinson’s disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including its in-home mobility features, could provide a digital biomarker that can be used to quantify how mobility changes as this disease progresses. To improve the effectiveness of current methods for indoor localisation, a transformer-based approach utilising multiple modalities, Received Signal Strength Indicator (RSSI) and accelerometer data from wearable devices, which provide complementary views of movement, is proposed. To properly evaluate our proposed method, we use a free-living dataset where the movements and mobility are greatly varied and unstructured as expected in real-world conditions. 12 pairs of people (one with PD, and the other a control participant) lived for five days in a smart home with various sensors. Our evaluation on such a dataset, which includes subjects with and without PD, demonstrates that our proposed network outperforms the current state-of-the-art in indoor localisation. We also show how the accurate room-level localisation predictions can be transformed into in-home mobility features (i.e. room-to-room transition duration) which can be used to effectively classify whether the PD participant is taking their medications or withholding them (increasing their symptoms)

Under review as a conference paper at ICLR 2023
INDOOR LOCALISATION FOR DETECTING MEDICA- TION USE IN PARKINSON'S DISEASE
Anonymous authors Paper under double-blind review
ABSTRACT
Parkinson's disease (PD) is a slowly progressive debilitating neurodegenera- tive disease which is prominently characterised by motor symptoms. Indoor localisation, including its in-home mobility features, could provide digital biomarker that can be used to quantify how mobility changes as this disease progresses. To improve the effectiveness of current methods for indoor localisa- tion, a transformer-basee approach utilising multiple modalities, Received Signal Strength Indicator (RSSI) and accelerometer data from wearable devices, which provide complementary views movement, is proposed. To properly evaluate our proposed method, are greatly varied and unstructured as expected in real-world conditions. 12 pairs of people (one with PD, and the other control participant) lived for five days in a smart home with various sensors. Our evaluation on such a dataset, which includes subjects with and without PD, demonstrates that our proposed network outperforms the current state-of-the-art in indoor localisation. We also show how the accurate room-level localisation predictions can be transformed into in-home mobility features (i.e. room-to-room transition duration) which can be used to effectively classify whether the PD participant is taking their medications or with- holding them (increasing their sympioms).
free-living dataset where the movements and mobility
INTRODUCTION
Parkinson's disease (PD) is a neurodegenerative disorder that affects around six million people worldwide. It is a chronic disease with four main symptoms: tremor, bradykinesia (slowness of movement), rigidity, and postural instability Jankovic (2008). Although PD is slowly progressive disease, the symptoms can fluctuate hourly depending on medication intake timing, stress and other factors. These fluctuations make it challenging to capture the slow symptom progression in an indi- vidual (needed for example to measure how a new therapy changes disease progression) accurately over time using infrequent clinic help a clinician understand the impact of PD on a patient, continuous monitoring is needed to give an accurate evaluation of how severe the symptoms and their fluctuations for that individual. One method to determine whether sensor based monitoring can detect symptom fluctuations in PD is by evaluating the patients in an "OFF" medication state (i.c. when they have not taken their medication) since, when they withhold medications, patients tend to experience a worsening of symptoms. For example, when "OFF" medications, motor symptoms can become more severe which may hinder the subject's 's gait and movement around their own home. As result, they may typically need more time to transition between rooms.
lab-based "snapshot" clinician-patien interactions. Also, to
Indoor localisation can be used to measure transitions between rooms in home. Knowing how slow or fast a person transitions between rooms may be able to predict whether a person an "OFF" medication state not and, longer-term, interpret their symptoms' severity (c.g. de novo freezing of gait in a hallway could indicate symptom progression) Bachlin et al. (2009). Localisation can also add context in the measurement of other behaviours such urinary function monitoring (e.g., how many times someone visits the bathroom overnight) which can impact the quality of life with HD et al. 2016))
To perform indoor localisation home environments, loT-based platforms with multimodal sensors combined with machine learning be used to provide unobtrusive and continuous localisation.

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Typically, wearable inertial measurement unit (IMU) is used produce received signal strength indicator (RSSI) at points throughout a home, which can be used to create radio-map features for room-level localisation. To provide more accurate localisation, accelerometer data from the same wearable can also be used as it provides a means to distinguish different activities ge.g., walking vs standing). As some activities kitchen), accelerometer data may enrich RSSI in differentiating adjacent rooms, which RSSI alone may struggle with Jovan et al. (2022).
tied to particular rooms (e.g. stirring pan on the hob must be in
If accelerometer data are to provide extra features for separating adjacent rooms, greater consider- ation must be given to data generalisation across different PD patients. As PD is a heterogeneous disease, the severity of symptoms may vary from one patient to another Grreenland et al. (2019). These severe symptoms, such as tremor, may affect the generalisation of accelerometer data, espe- cially those worn on the patient's wrists, which is common and well accepted placement location. Naively combining the accelerometer data with the RSSI may impair the performance of indoor localisation due to differing levels of tremor manifesting in the acceleration signal. In this work, we make two main contributions. (1) We describe the utilisation of RSSI enriched by the accelcrometer data to perform room-level localisation. Similar to Jovan et al. (2022). ligently chooses accelerometer features which may improve the RSSI performance in performing indoor localisation. To properly evaluate our proposed method, we use free-living dataset where the movements and mobility are greatly varied and unstructured as expected in real world condi- tions. Our evaluation on such a dataset, which includes subjects with and without PD, demonstrates that our proposed network outperforms the current state-of-the-ar DCMN (Jovan et al. (2022)) in all categories. (2) We also show how the accurate room-level localisation predictions can be trans- formed into in in-home mobility features (i.e. room-to-room transition duration) which can be used to effectively classify the "OFF" or "ON" medication state of PD patient.
proposed network intel-
RELATED WORK
Early work in machine learning for monitoring long-term neurodegenerative disease like PD started with a simple PD classification Fraiwan et al. (2016) or easy-to-distinuussh symptoms iden- tification Arora al. (2015); Fisher et al. (2016) using one single sensor modality such as vision sensors for simplicity Li et al. (0188). As the research progressed, multiple sensors have been con- sidered for performance improvement in identifying PD symptoms. For example, [Heidarivincheh et al. (2021) utilised vision and accelerometer sensors available in a smart home to classify PI) from non-PD.. Monn-PD.MMasee Masullo et al. (2020) match video sequences of silhouettes to accelerations from wearable sensors for a person re- ee-inenniifiationn in a home environment. Notably, all of this research uses vi- sion as their main data source; although powerful and rich in features, vision sensors typically raise privacy challenges in home settings and are typically absent in key rooms within the home for this reason.
RSSI data produced from wearable devices is one such mechanism with less privacy concern.. In indoor localisation, fingerprinting using RSSI is the typical technique used to estimate the location of wearable devices by exploiting this signal that can be measured in the environment. RSSI signals are not stable, they fluctuate randomly due to shadowing, fading and multi-patt effects. However, many techniques have been proposed in recent years tackle these fluctuations, and, indirectly, improve the localisation accuracy. Some of the work. Zhang et al. (016).. utilise deep neural network (DNN) to generate coarse positioning estimates from RSSI signals, which are then refined by hidden Markov model (HMM) to produce a final estimate location. Other works, Ibrahim et al. (2018), try to utilise time-series of RSSI data and exploit the temporal connections within each access point to estimate room-level position. A CNN is used to build localisation models to further leverage the temporal dependencies across time-series readings.
It has been suggested that we cannot rely on RSSI alone for indoor localisation in home environ- ments for PD subjects due to shadowing rooms with tight separation Pandey et al.) (2021). Jovan et al. (2022) et al. combine RSSI signals (for location estimate) and accelerometer data (for body movement estimate) to produce a more accurate location estimate by utilising a wider range of fea- tures to differentiate adjacent rooms. Their proposed network has shown an improvement in tracking a person with PD in a hallway that, in turn, can be used to track their transition time between rooms. Our work is inspired by Jovan et al. (2022) with two main improvements. While Jovan et al. capture
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"OFF" medications state for a period of several Under review as a conference paper at ICLR 2023
local temporal patterns of RSSI and accelerometer data via recurrent neural network, capture the patterns through the use of causal convolutional neural networks (CNN). In addition, unlike Jovan et al. who stop at predicting room-to-room transition durations, we go step further and use room- to-room transition behaviours, as input features to a simple binary classifier, to predict whether the PD participants is taking their medications withholding them.
COHORT AND DATASET
This dataset was collected using wristhand wearable sensors, one on each wrist of all participants, containing tri-axial accelerometers and 10 Access Points (APs) placed through the residential home, each measuring the RSSSowk et al. (2018). The wearable devices wirelessly transmit data using the Bluetooth Low Energy (BLE) standard which can be received by the 10 APs. Each AP records the transmitted packets from the wearable sensor which contains the accelerometer readings sampled at 30Hz, with each AP recording RSSI values sampled at Hz.
Dataset.
The dataset contains 12 pairs of participants living freely in a smart home for five days. Each pair consists of one person with PD and one person as the healthy control volunteer (HC). This pairing was chosen to enable PD vs HC comparison, for safety reasons for the person with PD, and also to increase the naturalistic social behaviour, particularly amongst the spousal pairs who already lived together. From the 24 participants, five females and seven males have PD. The average age of the participants is 58.5 and the average time since PD diagnosis for the person with PD is 11.3 years. To measure the accuracy ML models, wall-mounted cameras installed in the ground floor of the house as ground truth which capture red- ged-green-bee blue (RGB) and depth data during the day for 2-3 hours daily. These cover the kitchen, hallway, dining room, living room, stairs, and porch. The duration of data recorded by the cameras for PD and HC is 72.84 and 75.31 hours, respectively, which provides a relatively balanced label set for our room-level classification. Finally, evaluate the "OFF" medication state, participants with PD were asked to withhold their dopaminergic medi- cations so that they were in the practically-defi hours within five days.
Data prepprocessing. The two wearable sensors worn by each participant are grouped together based on their types, i.e., twenty RSSI values corresponding to 10 APs for each wearable sensor, and six spatial directions corresponding to three spatial directions (x, y, z) for each wearable, at each time. The accelerometer data is resampled to 5Hz to synchronise the data with RSSI value. With a 5-second time window, RSSI has an input of size (25 x 20) and accelerometer data has an input of size (25 6). Imputation for missing values, specifically for RSSI data, is applied by replacing the missing values with a value that is not possible normally (i.e., - 120dB). Both RSSI and accelerometer data were ignored when participants were out of the house. Finally, all timeseries measurements by the types are normalized to be within the range of zero and one before they are processed by a neural network model.
Our main focus is for our neural network model to produce in-home mobility features particularly for persons with PD. We hypothesize that during their "OFF" medication state, the deterioration in mobility of a person with PD is exhibited by how they transition between rooms. These features include 'Room-to-room Transition', and the 'Number of Transitions' between two rooms. With the layout of the house where participants stayed, the hallway is used as a hub connecting any other room on the ground floor, and 'Room-to-roo to-room Transition' shows the transition duration (in seconds) between two rooms connected by the hallway. The transition between (1) kitchen and living room, (2) kitchen and dining room, and (3) dining room and living room are chosen as the features due to their commonalty across all participants. For these features, we limit the transition time duration (i.e. the time spent in the hallway) to 60 seconds.
These features are produced by each model through the use of all available data from 12 PD partic- ipants including unannotated data from a.m. to 10 p.m. daily which are aggregated into 4 hour window. From this, each PD participant will have 20 samples (four samples for each day), each of which contains six features (three for room-to-roon room transition duration, and three for the number of room-to-room transitions), and only one of which represents "OFF" medication state. These fea-
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tures then fed to simple binary classifier' determining whether person with PD is their medication or not.
4 ROOM-LEVEL LOCALISATION WITH MULTI-SENSORY INPUTS
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(a) Base Model
(b) Proposed Model
Figure 1: Overview of the base model of Jovan et al. 2022 against our proposed model.
The Base Model. As our base model, we take an approach by Jovan et al. (2022 which proposes a technique utilise dual modalities (i.e. utilising different sensor types) for indoor localisation in home environments. The approach can accurately predict room-level localisation by reducing any room-to-room transition inaccuracies by integrating an modality (i.e. accelerometer data) and its temporal aspects into the main modality (i.e. RSSI data). This approach, which is called Dual Context Modality Network (DCMN), chosen due to its similarity in inputs (i.e. two types of sensors) and outputs (i.e. room-level localisation). It was shown to attain SOTA localisation performance on their dataset.
Suppose we have collection of RSSI signals x" = [xf...., x" = ix....., x E Ig'' within I time unit, where x; = [ x represents RSSI signals from r access points, and x = [x [ .......,,,, _r6 represents accelerometer data from a spatial directions at time with < T. During training, DCMN gets x" and x" as inputs, and produces reconstructed & ,
Txx and accelerometer data
RSSI signals &
Txx and embeddings of the predicted room locations (outputs before they go through Conditional Random Field layer) ex (ê..... èr] E Igd'xd with d as the embedding dimension, i.e.:
(6 x DCMN(XX xx)
Jovan et al. (2022) suggested ey is trained to generate the likelihood estimate of room predictions, while x used in an encoding fashion enhance the representation power of RSSI data. The final loss function is a combination of both likelihood and reconstruction losses:
C(eY, y, x²) = ( .y)
Σ
Cn(#),
with (.LL(.) represents the negative likeliild and CR(-) denotes the reconstruction loss.
The Issues. Although DCMN et al. (2022) has good attempt utilising multiple sensor data to perform indoor localisation, there are several issues with the technique that limit its performance:
The Lack of Local Context Matching with LSTM. Patterns in time series may evolve and can be repeated with time due various events (e.g. RSSI signals that fluctuate due
'The Random Forest is chosen binary classifier; It the used for indoor localisation.

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shadowing), whether observed point is anomaly (e.g. a sudden drop of one of the RSSI signals) or part of the patterns is highly dependent on its surrounding context. However, in LSTM layers, the local context is summarised based on the previous sum- marised context and the current input. Two similar patterns separated by a long period of time might have different context if they are processed by the LSTT layers] [Bahdanau al.. (2014). Furthermore, when two summarised contexts at different time points arrive at the MultiHead Self-Attention (MHSA) layer, even though they might be of similar patterns, the query-key matching of MHSA will not be able to capture the similarity due to their point-wise computation.
2. The Mix-Matched of Local Context Between Two Sensors. Figure la shows that the inputs to the modality enrichment layer are the summarised RSSI context and the sum- marised accelerometer context at each time step t. Since there is way to fix the length of local context that the LSTM can squeeze, the summarised context contain variable lengths of previous information. This varies only among different time step but also between RSSI signals and the accelerometer data at the same time step t.
3. Unnecessary Reconstruction Loss. In Equation 2 the reconstruction loss acts as a regu- larisation and also to capture the data distribution of the RSSI signals. However, to capture in in-home mobility which heavily relies on correctly predicting hallway, reconstruction loss might work against predicting hallway due relatively infrequent labels for the hallway compared to other room locations. We would, therefore, like to remove the use of recon- struction loss and add different loss that improves the accuracy of predicting particular room locations with less labels in home.
4.1 LOCALITY ENHANCEMENT WITH DUAL CONVOLUTIONAL SELF-ATTENTION
As it time series data, the importance of an RSSI or accelerometer value at each point in time can be identified in relation to its surrounding values such as cyclical patterns, trends, or fluctuations. Utilising historical context, that can capture local patterns on top of point-wist values, can thus lead to performance improvements in attention- based architectures. We consider to replace the LSTM on Figure Ta with a combination of causal convolution layers and a self-attention layers as Dual Convolutional Self-Attention (DCSA). The causal convolution layer was firstly introduced by Li jct al. (019) with kernel size k and stride I to extract local patterns using the same filter across all time. Note that causal convolutions ensure that the current position never has access to future information. The DCSA takes in a primary input &1 E RTxd and a secondary input &2 e T and yields:
($1, x2) = (RNNNNorm($(5)) + x1), Norm( + x2))
(3)
with
+(x) SA ( Wq (x) WK. & Wv)
(4)
where G.N is Gated Residual Network (GRN), introduced by Lim et al. 2019). to integrate dual inputs into one integrated embedding, Norm(.)
standard layer normalisation, SA(.) is scaled dot-product self-attention introduced in Vaswani et al. (2017). (x(.) is 1D-convolutional layer with a kernel size {1, and a stride 1, Wk E kædx W Q E Rdd Wy E gdxd are weights for keys, queries and values of the self-attention layer, and d is the embedding dimension. Note that all weights for GRN are shared across each time step t.
4.2 MULTIHEAD DUAL CONVOLUTIONAL SELF-ATTENTION
Similar to the base model, our approach employs self-attention mechanism introduced by Vaswani jet al. (2017) to capture global dependencies across time steps. It is embedded as part of the DCSA architecture. Inspired by Vaswani al. (2017) in utilising multi self-attention, with various kernel length with the same aim: allowing asymmetric long-term learning. The multi- head DCSA, shown as part in Figure lb takes in two inputs X1, &2 Txx and yields:
utilise our DCSA
MDCSA k. (x) 4x ky (........ (81, 82))
(5)
with
k kk.....k (8188) = SA(EE,,....
(2)
($1,82) WK, Ex, (81,82) Wv)
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and accelerometer Under review as a conference paper at ICLR 2023
(7) where (.) is a ID-convolutional layer with a kernel size {1, n) and stride n, WK E Rdxdd Wo E Rdxd Wv E Rdxd are weights for keys, queries and values of the self-attention layer, and E......... (.) concatenates the output of each DCSAA() in temporal order. For regularisation, a
Eki....... (81, 82) [DCSAR, (81,82).... DCSA,, (81, (8)
normalisation layer followed by a dropout layer is added after Equation [
Feature Transformation. Without LSTM layers acting as an encoder, a linear layer with a positional encoding is added transform both RSSI and accelerometer data into their respec- tive embeddings. The positional embeddings of RSSI h" = Ih.....,hh ,hl] h = [hf,.... h4] are then fed to an MDCSA network with various kernel size [Ri,....;Ai.] R..]
h MDCSA (
Kop (h", hh))
4.3 FINAL LAYER AND LOSS CALCULATION
We apply two different layers to produce two different outputs during training. The room-level predictions are produced via single conditional random field (CRF) layer in combination with linear layer applied to the output of Equation g to produce the final predictions as
U. CRF (Wphe + bp)
where W,, E Rdxm and by E Rm are weight and bias to learn, m the number of room locations, and h = [hj....,hy. h e xxd is the refined embedding produced by Equation
For the second layer, instead of trying to reconstruct the RSSI and comparing it with the actual RSSI signals, we choose a particular room as a reference and perform a binary classification at each time step t. The binary classification is produced via a linear layer applied to the refined embedding h as
3-- W Whh + b
(10)
where Wa 3 E Rdx1, and by E R are weight and bias to learn, and = [,.....,,3]] E IR. is the target probabilities for the referenced room within time window T. The binary classification loss can be formulated through the use of binary cross entropy loss function described as:
LECE LBCE(3,8) - + Σ B. log( (3.) (1 10---0 Bc) log(1 - (a:)
(11)
where 3 = 131, [31,...., (.. R' is the binary value whether at time / the room is the referenced room or not. The final loss function the Equation 2 with the Ln(()) being replaced by Equation 11
EXPERIMENTS AND RESULTS
We compare our proposed network with the base model Jovan et al. (2022) as a SOTA representa- tion for indoor localisation with dual modalities, Random Forest (RF) as a most baseline technique which has been shown to work well for indoor localisation, and TENER Yan et al. (2019) which is a modified transformer encoder in combination with a CRF layer. TENER represents a model with capability to capture global dependency and enforce dependencies across temporal aspects; hence, it can be considered as our algorithm without the locality enhancement it can be regarded as ablations. As a completion for the ablation study, we also include MDCSA, network when network only uses MDCSA with a single DCSA kernel of size O and "Ours RSST" network when our network only uses RSSI, without ACCL, its input features. For RF, all time series features of RSSI and accelerometer are flattened and merged into one long feature vector for room-level lo- calisation. For TENER, at each time step t, RSSI x and accelerometer x² features are combined via a linear layer before they are processed by the networks. A grid search on the parameters of each network is performed find the best parameter for each model. The parameters to tune are: the embedding dimension d in [128, 256). the number of epochs in (200, (200,300) and the learning rate in (0.01, 0.0001). The dropout rate is set to 0.15, and the RAdam optimizer Liu et al. (2019) in combination with Look-Aheer algorithm Zhang et al. (2019) is used for the training with early stopping using the validation performance. For RF, we perform a cross-validated parameter search for the number of trees (f200) 250)) and the minimum number of samples in a leaf node ((1, The Gini impurity is used to measure splits.
"Our regular network uses MDCSA147 with kemelss of size 1, 4, and 7

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Table Room-level and medication state accuracy of all models. Standard deviation is shown under (.), the best performer is bold, while the second best is italicized.
Room-Level Localisation
Medication State
Training
Model
Precision
F1-Score
F1-Score
AUROC
RF
95.00
95.20
56.67 (17.32)
84.55 (12.06)
TENER
94.60
94.80
47.08 (16.35)
67.74 (10.82)
MDCSA
94.80
95.00
62.11 (18.07)
80.20 (14.63)
ALL-HC
Ours RSSI
94.70
94.90
51.14 (11.95)
68.33 (18.49)
DCMN
94.80
95.00
47.25 (5.50)
75.63 (4.49)
Ours
94.90
95.10
64.13 (6.05)
80.95 (10.71)
Demographic Features
49.74 (15.60)
65.66 (18.54)
RF
89.67 (1.85)
88.95 (2.61)
54.74 (11.46)
69.24 (17.77)
TENER
90.35 (1.87)
89.75 (2.24)
51.76 (14.37)
70.80 (9.78)
MDCSA
90.67 (1.80)
89.99 (2.73)
64.70 (9.37)
77.26 (9.09)
LOO-HC
Ours RSSI
90.26 (2.43)
89.48 (3.47)
58.84 (23.08)
76.10 (10.84)
DCMN
90.52 (2.17)
89.71 (2.83)
19.56
73.26 (10.65)
Ours
91.39 (2.13)
91.06 (2.62)
55.50 (15.78)
83.98 (13.45)
Demographic Features
51.79 (15.40)
68.33 (18.43)
RF
86.89 (7.14)
84.71 (7.33)
43.28 (14.02)
62.63 (20.63)
TENER
86.91 (6.76)
86.18 (6.01)
36.04 (9.99)
60.03 (10.52)
MDCSA
88.02 (6.98)
87.60 (5.94)
51.89 (15.32)
71.07 (10.64)
LOO-PD
Ours RSSI
87.61 (6.64)
87.21 (5.44)
45.71 (17.85)
67.76 (10.73)
DCMN
87.36 (6.30)
86.44 (6.63)
44.02 (16.89)
69.70 (12.04)
Ours
88.04 (6.94)
87.82 (6.01)
49.99 (13.18)
81.08 (8.46)
Demographic Features
43.89 (14.43)
60.95 (25.16)
RF
74.27 (8.99)
69.87 (7.21)
50.47 (12.63)
59.55 (12.38)
TENER
69.86 (18.68)
60.71 (24.94)
N/A
N/A
MDCSA1
82.65 (7.06)
78.21 (8.03)
48.10 (19.71)
71.26 (12.43)
4m-HC
Ours RSSI
81.69 (6.85)
77.12 (8.46)
49.95 (17.35)
69.71 (11.55)
DCMN
78.79 (3.95)
71.44 (9.82)
43.89 (11.60)
64.67 (12.88)
Ours
83.32 (6.65)
80.24 (6.85)
55.43 (10.48)
78.24 (6.67)
Demographic Features
32.87 (13.81)
53.68 (13.86)
RF
71.00 (9.67)
65.89 (11.96)
N/A
N/A
TENER
65.30 (23.25)
58.57 (27.19)
N/A
N/A
MDCSA
80.89 (9.16)
75.60 (12.79)
48.81 (13.52)
76.28 (14.09)
4m-PD
Ours RSSI
77.47 (12.54)
73.99 (13.00)
41.79 (16.82)
67.37 (16.86)
DCMN
74.43 (9.59)
67.55 (14.50)
N/A
N/A
Ours
83.30 (6.73)
76.77 (13.19)
48.61 (12.03)
76.39 (12.23)
Demographic Features
36.69 (18.15)
50.53 (15.60)
Evaluation Metrics. We are interested in developing a system to monitor PD progression in home environments. For example, we will consider if there any significant difference the performance of the systems when trained on a person with PD versus trained on someone without. Doing this may provide useful insight whether there any benefit to a person with Parkinson's 's to train our model with training data from a healthy control (HC). We tailored our training procedure to test our hypothesis by performing variations of cross-vallin pooss-validations Apart from training our models on HC subjects (ALL-HC), we also perform four different kinds of cross-validation: 1) We leave one PD subject out as training data (LO0-PD), 2) we leave one HC subject out as training data (LOO-HC),
3) We leave one HC subject and use only roughly four minutes worth of data training (4m-HC), 4) We leave one PD subject and use only roughly four minutes worth of data as training (4m-PD). For all of our experiments, training data for LOO-PD and 4m-PD). For room-level localisation accuracy, we use precision and weighted Fi-score, all averaged and standard deviated across the test folds.
test trained models all PD subjects (excluding the one used as

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Table 2: Room-to-room transition accuracy of all models.
Data
Models
Kitchen-Live
Kitchen-Dine
Dive-Live
Ground Truth
18.71 (18.52)
14.65 (6.03)
10.64 (11.99)
RF
16.18 (12.08)
14.58 (10.22)
10.19 (9.46)
TENER
15.58 (8.75)
16.30 (12.94)
12.01 (13.01)
MDCSA
16.83 (17.31)
14.91 (9.76)
9.81 (10.12)
ALL-HC
Ours RSSI
15.73 (7.51)
15.00 (10.64)
11.82 (12.46)
DCMN
15.27 (7.51)
13,40 (6.43)
10.84 (10.81)
Ours
17.70 (16.17)
14.94 (9.71)
10.76 (9.59)
RF
17.52 (16.97)
11.93 (10.08)
9.23 (13.69)
TENER
14.62 (16.37)
9.58 (9.16)
7.21 (10.61)
MDCSA
17.68 (16.79)
14.14
10.07 (13.55)
LOO-HC
Ours RSSI
17.06 (15.34)
14.38 (8.27)
11.04 (14.20)
DCMN
16.30 (17.78)
14.01 (8.08)
10.37 (12.44)
Ours
17.70 (17.42)
14.34 (9.48)
11.07 (13.60)
RF
14.49 (15.28)
11.67 (11.68)
8.65 (13.06)
TENER
13.42 (14.88)
10.87 (10.37)
6.95 (10.28)
MDCSA
15.60 (14.02)
14.16 (10.22)
8.89 (12.30)
LOO-PD
Ours RSSI
15.60 (14.21)
13.21 (9.65)
10.07 (12. 18)
DCMN
16.98 (15.15)
15.26 (8.85)
9.99 (13.03)
Ours
16.42 (14.04)
14.48 (9.81)
10.77 (14.18)
RF
14.22 (18.03)
11.38 (15.46)
13.43 (18.87)
TENER
10.75 (15.67)
8.59 (14.39)
N/A
MDCSA
19.38 (19.43)
16.34 (16.78)
13.76 (17.47)
4m-HC
Ours RSSI
18.07 (16.78)
16.72 (14.62)
13.84 (18.36)
DCMN
16.89 (18.07)
14.68 (13.57)
9.31 (15.70)
Ours
18.15 (19.12)
15.32 (14.93)
11.89 (17.55)
RF
11.52 (16.07)
8.73 (12.90)
N/A
TENER
8.75 (14.89)
NIA
N/A
MDCSA
19.58 (18.62)
14.30 (12.32)
13.47 (18.50)
4m-PD
Ours RSSI
19.00 (17.78)
16.12 (13.70)
13.34 (17.69)
DCMN
14.75 (13.79)
13.47(17.66)
N/A
Ours
17.96 (19.17)
14.74 (10.83)
10.16 (14.03)
To showcase the importance of in-home mobility features in differentiating the medication state of person with PD, we first compare how accurate the -Room-to-room Transition' duration produced by each network to the ground truth. We hypothesis that the more accurate transition the ground truth the better mobility features are for medication state classification. For the medication state classification, we then compare two different groups features for each network: 1) the baseline demographic features such age, gender, years of PD, and PD assessment score specifically the Movement Disorders Society-sponsored revision the Unified Parkinson's s Disease Rating Scale motor subscore (MDS-UPDRS III), and 2) the normalised in-home mobility features. The metric we use for "ON" / "OFF" medication state evaluation is the weighted F1-Score and AUROC which averaged and standard deviated across the test folds.
5.1 EXPERIMENTAL RESULTS
Room-level Accuracy. The first part of Table compares the performance of our network and any other network for room-level classification. For the room-level classification, our network out- performs other networks and RF with minimum improvement of 1.3% for the Fl-score over the second best network (i.e. DCMN) in each cross-validation type with the exception HC validation. The improvement is more significant the HC and 4m-PD validations, when the training data are limited, with an average improvement at almost 9% for the FI Fc-score over the DCMN.
the ALL-
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Under review as a conference paper at ICLR 2023
The LOO-HC and LOO-PD validation show that model that has the ability to capture the temporal dynamics across time steps (e.g. TENER and DCMN) will perform better than a standard baseline technique such as Random Forest. DCMN performs better in those two validations due to its ability to capture asynchronous relation across modalities [Jovan et al. (002). However, when the training data become limited as in 4m-HC and 4m-PD validations, having extra capabilities is necessary to further extract temporal information and correlations. Due to being vanilla transformer requiring considerable amount of training data, TENER performs worst in these two validations. DCMN performs quite well due to its ability to capture local context via LSTM for each modality and suppress a noisy modality, i.e noisy accelerometer data via GRN. Our proposed network has all the capabilities that DCMN has with an improvement in the local context matching across modalities via DCSA. The local context matching capability seems to have a big impact in maintaining the performance of the network when the training data is limited. It is shown by how the MDCSA outperforms the DCMN by an average of 7% for the Fl-score in 4m-HC and 4m-PD validations.
Room-to-room Transition and Medication Accuracy The second part of the Table shows the performance of all our networks for medication state classification. The Demographic Features can be used as baseline for each type of validation. Our network, with the exception of the ALL- HC validation, outperforms any other network by a significant margin for the AUROC score. A minimum of 15% improvement over the baseline demographic features can be obtained by using the in-home mobility features produced by our model trained on ALL--C training data. In 4m-PD) validation data, demographic features show that its performance is not better than a simple random binary function with an AUROC score of 50.53% while our proposed network manages to achieve a 76.39% AUROC score with just 7.5% down from its best score in the LOO-HC validation. In the same validation, RF, TENER, and DCMN could not manage to provide any prediction due to their inability to capture (partly) hall transitions which are the main features for data samples for medication state classification. Furthermore, TENER model has shown its inability to provide any medication state prediction from the 4m-HC data validations. It can be traced back to Table Where the TENER model can not capture any transition between dining room and living room across all PD participants resulting incomplete features for all samples in 4m-HC validations. Our proposed network (and all its variations) are able to provide medication state prediction and maintain its performance across all cooss-alidations thanks to the addition of Equation T1 in the loss function calculation which forces the model to predict correctly the hall transitions. This is validated in Table 2 which shows that our proposed network, on average, has the closest transition duration prediction the ground truth beating the second best by around 0.5 seconds across all hall transitions
6 CONCLUSION
We have presented a new approach for indoor localisation utilising RSSI and accelerometer data. Our approach is based on (1) novel transformer structure, which is carefully designed to take dual time-series inputs in combination with an CRF layer enforcing correct transition among time steps, and (2) an alteration to the loss function proposed in Jovan et al. (0022) to further strengthen the room-to-room transition prediction. We demonstrated that our proposed model is able to outperform the Dual Modality Context Network (DCMN) et al. (2022), and achieve SOTA results in room- level predictions on data collected of people with and without PD living freely in a smart home.
Within naturalistic settings, in-home mobility can be measured using our proposed approach, and we hypothesize that a person with PD during their "OFF" medication state shows signs of slowing in how they transition between rooms in their house. We show that, through our network which is a combination of multiple dual convolutional self-attention (MDCSA) and a CRF model, having an accurate indoor localisation system will give accurate in-home mobility features. This will lead to an accurate classifier model which can differentiate succinctly if a person with PD is in an "ON" or "OFF" medication state. We demonstrated that our proposed model able to produce accurate in-home mobility features, which result in outperforming in- home mobility features produced by other models with an average improvement over 10% the second best in AUROC score.
'The average ignores the 4m-PD validations since RF and DCMN does not have (some) hall transitions
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Under review as a conference paper at ICLR 2023
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Mare Bachlin, Daniel Roggen, Gerhard Troster, Meir Plotnik, Noit Inbar, Inbal Meidan, Talia Her- man, Marina Brozgol, Eliya Shaviv, Nir Giladi, and Jeffrey M. Hausdorff. Potentials of enhanced context awareness in wearable assistants for parkinson's 's disease patients with the freezing of gait syndrome. In 2009 International Symposium on Wearable Computers, pp. 123-130, 2009. doi: 10.1109/1SC..2000.1
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate, 2014. URL htttts:://aatt www.xii.rrrr/oooooooooooo 1409. 0473
James M. Fisher, Nils Hammerla, Thomas Ploetz, Peter Andras, Lynn Rochester, and Richard W. Walker. Unsupervised home monitoring of parkinson's 's disease motor symptoms using body-worn accelerometers. Parkinsonism & Related Disorders, 33:44-50, 2016. ISSN 1353-8020.
Luay Fraiwan, Ruba Khnouf, and Abdel Razaq Mashagbeh. Parkinson's disease hand tremor de- tection system for mobile application. Journal of Medical Engineering & Technology, 40(3): 127-134, 2016.
Julia C. Greenland, Caroline H. Williams-Gres and Roger A. Barker. The clinical heterogeneity of parkinson's disease and its therapeutic implications. European Journal of Neuroscience, 49(3): 328-338. 038, 2019.
Lu He, Eslinger, and Xuemei Huang. The key determinants to quality of life in parkinson's 's disease patients: Results from the parkinson's disease biomarker program (pdbp). Journal of Parkinson's disease, 633):523-532, 2016.
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Farnoosh Heidarivincheh, Ryan McConville, Catherine Morgan, Roisin McNaney, Alessandro Ma- sullo, Majid Mirmehdi, Alan L. Whone, and Ian Craddock. Multimodal classification of parkin- son's disease in home environments with resiliency to missing modalities. Sensors, 21(12), 2021.
Mai Ibrahim, Marwan Torki, and Mustafa ElNainay. Con based indoor localization using rss time- series. In 2018 IEEE Symposium on Computers and Communications (ISCC), pp. 01044-01049, 2018. doi: 10. 100.1099....2.1.888538000
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Ferdian Jovan, Ryan McConville, Catherine Morgan, Emma Tonkin, Alan Whone, and Ian Crad- dock. Multimodal indoor localisation for measuring mobility in parkinson's 's disease using trans- formers, 2022. URL
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Michael H Li, Tiago A Mestre, Susan H Fox, and Babak Taati. Vision-based assessment of parkin- sonism and levodopa-induced dyskinesia with pose estimation. Journal of neuroengineering and rehabilitation, 15(1):1-13, 2018
Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In H. Wallach. H. Larochelle, A. Beygelzimer, d'Alché- Buc, E. Fox, and Garnett (eds.), Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
Bryan Lim, Sercan O. Arik, Nicolas Loeff, and Tomas Pfister. Temporal fusion transformers for interpretable multi-horizon time series forecasting, 2019.
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Reviews:
[{"review_id": "w4MNwvyNv8Y", "reviewer": "Reviewer_NCB2", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper discusses a method for detecting the evaluation Parkinson’s disease and the patients’ use of medication by monitoring the motion of patients at home. This is done by indoor localization at room level fusing Bluetooth RSSI measurements and accelerometer (or actually whole IMU?) measurements. The paper builds on state-of-the-art method based on Dual Context Modality Network by replacing its LSTM network with causal convolutional layers and self-attention (DCSA).", "summary_of_the_review": "The paper is interesting, but it includes technical errors. Although the developed method is relevant, the experiments and its analysis leave more questions than give answers. ", "strength_and_weaknesses": "The paper addresses relevant topic, and the developed method seems interesting and technically sound.  However, the experiment setup and results raise many questions. Is it enough to see how the medication affects the patient’s motion in five days?  Transfer between some rooms is inferred correctly, whereas for some not. The interpretation of the results is not very clear. The results show that the method outperforms a benchmark that has been considered as the state-of-the-art method. However, some more traditional methods outperform the developed one. Therefore, the effect seems to be quite incremental.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "vZSSDTIgUt", "reviewer": "Reviewer_r87h", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes a method to improve indoor localization based on combination of RSSI and accelerometer data from waerable device. The localization data is used to produce features that serve as inputs for a classification model aiming to recognize when patients with Parkinson disease are off their medication. The analysis is performed or real data collected from PD patients and healthy control subjects living in a smart home. The localization model is based on the previous work of Jovan et al. 2022 changing their NN architecture from RNN with attention to causal convolution with attention. Furthermore, they complement the outputs at the room-identification level and room-transition counts by the actual on/off medication classification, reporting favorable results. ", "summary_of_the_review": "The paper addresses an interesting and important application problem. However, its contribution is marginal mainly due to being largely based on on work of Jovan et al. 2022 bringing little new. Neither the dataset nor the method have been released and therefore cannot serve the community for further investigation. I can imagine the paper being of interest to the specific application community focusing on PD patients. In my view, however, it does not have the potential to trigger interest from the ICLR community and does not contribute enough to be considered for publication. I therefore recommend to reject the paper.", "strength_and_weaknesses": "Strength:\nThe paper has a strong motivation of using indoor localization data to improve non-intrusive health monitoring of PD patients.\nIt uses data from a well designed experimental setup with real patients and control subjects.\nIt seems to perform slightly better than previous methods\n\nWeaknesses:\nThe contribution of the paper is rather little - it largely bases on previous paper of Jovan at all 2022, reusing their motivation, problem definition as well as data.\nThe method is only slightly modified reaching small, insignificant performance improvement.\nThe paper is not reproducible (see next section)", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "kmYwgWDfus", "reviewer": "Reviewer_Q2sw", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This work proposes an approach for indoor localisation utilising RSSI and accelerometer data based on transformers with CRF layer an alternating loss function. Its contributions are mostly in the specific application (indoor localisation) rather than the methods.", "summary_of_the_review": "I believe this paper would be a better match in a biomedical/mobile computing venue.", "strength_and_weaknesses": "S\n\n+ important ML application\n+ solid analysis of the dataset (albeit small for modern ML)\n\n\nW \n\n+ limited model novelty (reusing existing parts)\n+ limited new insights for the representation learning community", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "_EOoX3QOjL", "reviewer": "Reviewer_Ktqs", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper proposes a new model to evaluate Parkinson's disease feature extraction. In particular room-to-room classification. I'm not sure if the paper proposes a new dataset, but I think they do (it is not clear from the writings). In the experimental sections there's very close performances and very high standard deviations, it is unclear to me if the proposed methods works better. I have the following more detailed comments below:\n\n\"Indoor localisation, including its in-home mobility features\" this sentence does not make sense to me.\n\"To improve the effectiveness of current methods for indoor localisa-\ntion, a transformer-based approach utilising multiple modalities, Received Signal\nStrength Indicator (RSSI) and accelerometer data from wearable devices, which\nprovide complementary views of movement, is proposed.\" please rephrase this is hard to read.\n\"To properly evaluate our proposed method, we use a free-living dataset where the movements and mobility are greatly varied and unstructured as expected in real-world conditions. \" What's a free-living dataset? is this a standard to define such a dataset? please provide references in your commentary.\n\"12 pairs of people (one with PD, and the other a control participant) lived for five days\nin a smart home with various sensors.\" Is this your own dataset? what is \"various sensors\"?\nReading the abstract it's clear that the authors are either novices, not native english speakers, or the PI didn't bother to help writing it. It's cluttered and cumbersome to digest. It should be clear, crisp, and spot-on.\n\n\"Although PD is a slowly progressive disease, the symptoms can fluctuate hourly depending on medication intake timing, stress and other factors.\" citation? where do you get this information from?\n\n\"These fluctuations make it challenging to capture the slow symptom progression in an individual (needed for example to measure how a new therapy changes disease progression) accurately over time using infrequent clinic or lab-based “snapshot” clinician-patient interactions. \" this sentence is hard to read, what are you trying to say?\n\n\"Also, to help a clinician understand the impact of PD on a patient, continuous monitoring is needed to give an accurate evaluation of how severe the symptoms and their fluctuations are for that individual.\" While I can intuitively think that this might be reasonable for a practitioner to know this information, I want a research study motivating it. If not, why are you then doing this research project?\n\n\"Indoor localisation can be used to measure transitions between rooms in a home\" can it? who says that? again, if you don't back up your statements with research papers I can't trust them. I don't know by default the state of the indoor localisation field. Moreover, the trailing citation is one 13 year old symposium paper on the \"potential\" of contextual awareness from wearables. I would expect a, somewhat recent, clinical journal paper formalizing the treatment procedure of Parkinson.\n\n\"To perform indoor localisation in home environments, IoT-based platforms with multimodal sensors combined with machine learning can be used to provide an unobtrusive and continuous localisation.\" I'm sure they can, but you need to prove it. Please provide a paper documenting this feature.\n\n\"Typically, a wearable inertial measurement unit (IMU) is used to produce a received signal strength indicator (RSSI) at points throughout a home, which can be used to create radio-map features for room-level localisation.\" If it's a typical setup, then you should have plenty of studies, or protocols, detailing it. Provide these.\n\nAt the end of your introduction, it seems like you work off the Jovan 2022 paper. It's not clear to me how you differ from their research findings. Make your motivation more clear in the introduction for what motivated your to alter their method, what you changed, why you believed those changes are important, and how much better you perform.\n\n\"Early work in machine ...\" you mention \"simple PD classification\". I'm not a PD researcher, I don't know what that means. This sentence does not make it clear to me how the initial models on PD prediction operates.\nFor the vision models you mention, what are they predicting? Why were they better? Is the main concern privacy?\n\n\"RSSI signals are not stable, they fluctuate randomly due to shadowing, fading and multi-path effects.\" I need a citation here.\n\nWhat wearable are you using in your study, is it a fitbit? and apple watch?\n\n\"To measure the accuracy ML models\" I don't understand this\n\nWhat did you use the camera data for? did a person manually sit and look it through to annotate the gold label for when participants when to different rooms? how did this work at night?\n\nAre you preprocessing steps standard in the field? why don't you cite any papers?\n\nYour data preprocessing is not completely clear to me.\n\nFor your dataset, did you collect it yourself, or where is it from? if you collected it yourself, where's your IRB? Why did you need to gather a new dataset, was it just for medicatio ON/OFF?\n\nThe math explaining your model is confusing, it doesn't give me a clear overview of how your model differs from the Jovan 2022 paper. As far as I understand your modification is applying a CNN layer at the start and a CRF layer at the end?\n\nTable 2, ALL-HC ours the performance reported is 17.70 (16.17). Does that mean you have 17.70 mean accuracy and 16.17 standard deviation?\n\nHow many of your results are statistically significant?", "summary_of_the_review": "At this stage, I do not believe this article is suitable for publication.  Moreover, their results do not seem significant and their standard deviations are very large.", "strength_and_weaknesses": "Strength:\nNew dataset with medication states (maybe?)\n\nWeaknesses:\nPoor writing\nlack of results\ndifficult to understand motivation for methodical changes to their model", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject